Field Personnel Research Articles

Reasons for Declining Consent in a Population-Based Cohort Study Conducted in a Rural South American Community.

There is limited information on participants' adherence and reasons for declining consent in observational cohort studies conducted in remote rural communities. We aimed at sharing lessons learned during the Atahualpa Project, a population-based cohort study conducted in a rural Ecuadorian village. Atahualpa residents aged ≥40 years identified during door-to-door surveys who signed a consent form were enrolled. Annual surveys were conducted to assess the number of participants who moved out of the village, as well as those who died, declined consent, and newly entered the study. Reasons for declining consent were tabulated. ed data included age, sex, education, disability, time between enrollment and declining consent, and reasons for withdrawal. We also counted participants who, despite expressing their willingness to continue in the study, refused specific procedures. After five years of follow-up, 54 (6.3%) of 863 enrolled individuals declined consent. Increasing age and disability had no impact on declining consent. In contrast, refusal was higher among relatives or neighbors of a given participant declining consent. Most people who declined consent did so after one or two years of enrollment. Less than 20% of enrolled individuals refused certain procedures. “Fear of the needle” was the most frequent reason for refusing blood tests, and common reasons for declining complimentary exams were lack of interest and time constraints. Cohort retention in the Atahualpa Project is high. Main reasons for this adherence include adequate selection of the village, detailed planning of procedures, assurance of sponsorship, and field personnel who continuously engage with study participants. This trial is registered with NCT01627600.

Application of Intelligent Terminal to Power Monitoring System

Owing to the new forms of big data, the bottlenecks of supervision technologies on field monitoring have become increasingly obvious and the relevant system response speed is hard to satisfy the practical application requirements. In addition, numerous application scenarios can no longer be supported with traditional monitoring methods. This paper introduces the traditional monitoring methods, and analyzes the advantages and disadvantages. In particular, the superiorities of intelligent terminal are expounded. By using the front-end intelligent equipment, the information of the field environments can be grabbed dynamically, and the intelligent image processing technology can be combined to realize the real-time recognizing, and tracking of the field environments and personnel information, thus reducing the probability of the occurrence of the work accidents.

Scalable network analytics for characterization of outbreak influence in voluminous epidemiology datasets

SummaryPlanning for large‐scale epidemiological outbreaks in livestock populations often involves executing compute‐intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this study, we set out to achieve two goals, ie, (1) providing a distributed framework for modeling disease transmission at scale using Spark, including improvements to the default GraphX partitioning strategy, and (2) giving planners and epidemiologists a means to analyze interactions between entities (herds) during simulated disease outbreaks. Using our disease transmission network (DTN), planners or analysts can isolate herds that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel. We use a representative dataset to verify our approach and optimized the underlying graph partitioning algorithm to ensure the system will scale with increases in the dataset size or number of participating machines. Our analysis includes identification of influential herds as well as the creation of machine learning models for accurate classifications that generalize to other datasets.

AI Firm Ambyint’s New Bakken Deal With Equinor Moves the Industry Another Step Closer to the Edge

In the face of leaner economic times, oil and gas companies want to be able to boost well counts while minimizing any new additions to their field workforce. Among the companies answering this call is Ambyint, which was just tapped to deploy its artificial intelligence (AI) systems to help optimize all of Equinor’s Bakken Shale wells running on sucker rod pumps—the oil field’s most common breed of artificial lift. The deal is understood to be one of Ambyint’s largest contracts to date. Specific numbers have not been shared, but public data show that Equinor operates more than 800 wells in North Dakota. Ambyint, which is headquartered in Calgary and has an office in Houston, said the project scope may eventually include all of the Norwegian-owned operator’s horizontal wells in the state as they transition to rod pump. The large-scale upgrade marks another milestone in the evolution of oilfield automation that for decades has been defined by a nearly-ubiquitous reliance on SCADA (supervisory control and data acquisition) systems. One of the drawbacks of the status quo is that it requires small armies of field personnel to interpret SCADA data and then adjust setpoints to get pumping units back into optimal operating ranges. This manual process can consume half an hour per well to complete; downtime that quickly adds up in a field of hundreds. “What we are talking about is having the machine do that entire workflow,” Chris Robart, Ambyint’s president of US operations said. By equipping wells with its cloud-based AI and edge computing technology, and an application the company calls an autonomous setpoint management system, the man-hours once spent re-setting pumps can hopefully be reallocated to other bottom-line drivers. “We are freeing up individuals to go do other things, like think about new technology, troubleshoot failed equipment, deal with workovers, or new well designs,” Robart added. Pilot Hits the Mark for Equinor The Bakken project comes after a pilot that included 50 of Equinor’s wells, which saw a net production increase of 6%—considerably larger uplift figures were seen from those wells suffering from underpumping. The encouraging results were realized with zero shutdowns in production and minimal “human interference,” according to the companies. “The Ambyint technology has improved the remote data visibility and has delivered a more accurate diagnostic of downhole conditions to our rod pump wells in the Bakken,” a production engineer for Equinor’s Bakken asset, Jack Freeman, said in a statement. “The autonomous speed range management tool has leveraged the power of machine learning to optimize our wells by identifying and acting on real opportunities” Last year, Ambyint’s Series A funding round raised $11.5 million from several venture capital groups including Equinor Technology Ventures (formerly known as Statoil Technology Invest).

Effect of Biomass Fuel Exposure on Pregnancy Outcomes in Rural Bangladesh

Low birth weight is a major cause of infant morbidity and mortality in low-income countries like Bangladesh. Emerging data suggest that exposure to biomass fuel(BMF) smoke, common during cooking with BMF, might affect birth weight. Here we investigated whether exposure to BMF smoke is associated with pregnancy outcomes in rural Bangladesh.As part of the Cook Stove Study of Bangladesh(CSSB) we recruited 903 pregnant women within 6-18 weeks of pregnancy. Detailed sociodemographic, health, diet, and pertinent exposure data (type of fuel used, duration of cooking, kitchen type and others) were collected by trained interviewers. Anthropometric measurements of the newborns(N=797) were done by field personnel within 24-72 hours of delivery. This analysis was restricted to newborns born within 37-42 weeks of gestation(N=736). Participants who used coal, wood, leaves, agricultural residues, or cow dung were categorized as solid fuel users(n=602). We fitted a multivariable linear regression model to assess the effect of exposures of interest on birth weight, length and head circumference after adjusting for gestational age, and maternal age, BMI, diabetes status and hypertension.The mean(±SD) birthweight of the newborns was 2736 gm(±370gm). Almost 82% of the women used solid fuel for cooking and spent on average 3.32hrs(±1.8hrs ) for cooking. We observed a statistically significant interaction for the use of solid fuel and duration of cooking on birth weight(p =0.03) and marginally significant interaction on birth length (p =0.1). Based on hour model coefficients, 3.32 hours of cooking with solid fuel can lead to a 145gm reduction in birthweight, compared to a woman who does not cook or spend time in the kitchen during cooking. Exposure of pregnant women to solid fuel smoke during cooking has a detrimental effect on birth weight of the newborn.

Closed Course Performance Testing of a Work Zone Intrusion Alarm System

Oldcastle Materials, Inc. and ARTIS, LLC joined forces to develop an innovative work zone intrusion detection and alarm system. The alarm system flashes light-emitting diodes and emits an audible alarm if an intrusion is detected. Texas A&M Transportation Institute performed a closed course study to assess performance of the alarm system under conditions that may occur in real work zones. This testing was intended to verify that the alarm system does produce the proper alert when conditions warrant it and that alerts were not activated when conditions did not warrant them (i.e., that the system does not produce false alarms). Performance of the worker body alarms (designed to be worn by field personnel and activated when an intrusion threat is detected) was also evaluated. During testing, these devices were positioned at various locations to assess the ability of the system to correctly determine the location of the devices and their position relative to the intrusion threat. The alarm system was tested under two operating modes: lane closures and flagging operations. Several approach vehicle trajectories were used in both operating modes. The main alarm achieved a 100% success rate, whereas the worker body alarms achieved a 97% success rate. Unsuccessful events for the worker body alarms were attributable to the test protocol exceeding the effective communication range of the intrusion detection system. Ongoing improvements in the device are expected to significantly increase this communication range. All other device locations, scenarios, and trajectories tested achieved a 100% success rate.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

Rod-Pump Optimization: Big Data Helps Accelerate and Improve Operations

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181216, “Proactive Rod-Pump Optimization: Leveraging Big Data To Accelerate and Improve Operations,” by Tyler Palmer, SPE, and Mark Turland, SPE, Denbury Resources, prepared for the 2016 SPE North American Artificial Lift Conference and Exhibition, The Woodlands, Texas, USA, 25–27 October. The paper has not been peer reviewed. This paper presents how a US onshore operator took a three-step approach to optimize more than 100 rod-pump wells. The approach involved data consolidation, automated work flows, and interactive data visualization. This approach led to increased unit run times, decreased unit cycling, improved production and equipment surveillance, and increased staff productivity. The ultimate goal was to increase profitability by decreasing lifting costs and increasing operating efficiency. Introduction The processes and tools described in this paper cover a subset of approximately 125 wells in eastern Montana and western North Dakota, but they have been designed to be applicable and scalable to any fields that use rod-pump artificial-lift systems with supervisory control and data acquisition (SCADA). Simple modifications can be made to the tools and processes for wells that do not have SCADA capabilities. While optimization efforts and best practices have been implemented for the subject rod-pump systems during the past 6 decades, many opportunities remain to create additional value. Empirical knowledge from field personnel serves as the basis for the analytical model. Categorizing and quantifying the observations made by the field personnel is critical to developing any analytical model involving oil and gas operations. On the basis of feedback from field personnel and engineers, the following areas had the most potential for improvement: data consolidation, automated work flows, and data visualization. The data-consolidation issue stems from data being located in multiple file locations, sometimes being stored in nontabular formats and initially lacking the necessary unique identifiers for mapping between databases. Automated work flows were essentially non-existent; wells were analyzed individually using deterministic, static data. Data were previously visualized in multiple locations but never integrated into a single, interactive visualization tool. The opportunities to maximize asset value led to the development and implementation of the rod-pump optimization tool (RPOT). The RPOT is a data-visualization tool that generates a single recommended optimization action (ROA) for each well being analyzed. The ROA logic calculates the optimal amount of fluid for a well to produce on the basis of its inflow, while accounting for surface and subsurface equipment constraints. General examples of ROAs include slowing wells down by making a specific sheave adjustment, speeding wells up by a specified strokes/minute (spm) amount, upsizing the downhole pump to a specific pump size, or upsizing or converting to a different artificial-lift system. If in accurate or incomplete data are brought into the database, the ROA specifies the data source that needs to be quality-checked.

<b>Sighting trend of the Indian Skimmer (Charidiformes: Laridae: <I>Rynchops albicollis</I> Swainson, 1838) in National Chambal Gharial Sanctuary (1984–2016) reflecting on the feasibility of long-term ecological monitoring</b>

River Chambal, in northwestern India, is a tributary of the Gangetic River system. It flows through the states of Rajasthan, Madhya Pradesh (MP) and Uttar Pradesh (UP), and the National Chambal Gharial Sanctuary (NCGS) encompasses a 572km stretch of the river from Keshoraipatan in Rajasthan to Pachhnada in UP. The sanctuary includes about 15km of river Yamuna after confluence with Chambal. During annual monitoring of Gharial in Chambal the Indian Skimmer, Rhynchops albicollis numbers were also counted in 12 study zones for 17 observation years spread between 1984–85 and 2015–16. The number of skimmers was below 355 for 15 of the 17 observation years. Skimmer counts were higher in 1995 at 555 individuals. The count for different study zones is not uniform, but with reference to Rajghat at the crossing of National Highway number-3, the count upstream is lower than downstream with an exception in the year 2011. The river downstream appears to be a better skimmer habitat with better availability of fish, and long stretches of flowing water along low-lying sandy banks. The study calls for continued, coordinated and strengthened attention to NCGS for added conservation of locally migrating birds. The research and management connect in NCGS is a model where long term ecological monitoring has been possible because of simple and implementable protocols that were used unchanged over such a long period by a set of identified field personnel.

Relative occurrence of the family Kalotermitidae (Isoptera) under different termite sampling methods

The termite family Kalotermitidae constitutes a wood-nesting termite family that accounts for about 15% of all extant termite species. In recent decades, field studies have been carried out to assess termite diversity in various wooded habitats and geographic locations. Three sampling methods have been favored expert, transect, and alate light-trap surveys. Expert collecting is not spatially quantifiable but relies on field personnel to recognize and sample termite niches. The transect method aims to standardize and quantify termite abundance and diversity. Light trapping is a passive method for sampling nocturnal alate flights. We compared our expert survey results and results of published sampling methods for their proportional yields of kalotermitid versus non-kalotermitid encounters. Using an odds ratio statistic, we found that worldwide, there is about a 50.6-fold greater likelihood of encountering a kalotermitid sample versus a non-kalotermitid using the expert survey method and a 15.3-fold greater likelihood using alate trapping than using the transect method. There is about a 3.3 -fold greater likelihood of collecting a kalotermitid specimen versus a non-kalotermitid sample using the expert survey method than using the alate trap method. Transect studies in which only termite species diversity was reported gave similar low Kalotermitidae yields. We propose that multiple biases in sampling methodology include tools, time constraints, habitat type, geographical location, topographical conditions, and human traits account for the divergent outcomes in sampling the abundance and diversity of Kalotermitidae compared to other termite families.

Utility of Automated Species Recognition For Acoustic Monitoring of Owls

Presence or abundance of owls is frequently assessed using call-broadcast surveys to elicit responses and increase detection rates, but can draw owls in from a distance and could affect conclusions about fine-scale habitat associations. Passive acoustic surveys with field personnel or autonomous recording units (ARUs) may be a less biased method for surveying owls. Automated recognition techniques have proven useful to process large volumes of acoustic recordings from ARUs, and we sought to test the utility of automated recognition for three owl species. We built templates or “recognizers” for the territorial calls of the Barred Owl (Strix varia), the Boreal Owl (Aegolius funereus), and the Great Horned Owl (Bubo virginianus). We assessed the performance of each recognizer by evaluating precision, processing time, and false negatives. We used ARUs to survey for owls in northeastern Alberta, Canada, and compared the results from the recognizers to results from researchers listening to a subsample of the recordings. We verified the results to filter out false positives, but verification time was substantially lower than time spent listening. We processed more recordings and obtained a larger dataset of owl detections than would have been possible with either listening to the recordings only or conducting traditional field surveys without ARUs, suggesting a significant benefit of automated recognition. Precision was quite variable, but false negatives were relatively low and did not affect results of owl habitat associations. Given the relatively low detection rates of owls by listening to recordings, an automated recognition approach is likely to be highly useful for monitoring owls.

Thunderstorm Characteristics during the Ontario Winter Lake-Effect Systems Project

AbstractThe Ontario Winter Lake-Effect Systems (OWLeS) field campaign during the winter season of 2013/14 provided unprecedented data with regard to the structure and behavior of long-lake-axis-parallel (LLAP) lake-effect storms. One of the interesting characteristics of LLAP storm bands is their ability to initiate lightning. The OWLeS datasets provide an opportunity to examine more thoroughly the kinematics and microphysics of lake-effect thunder-snowstorms than ever before. The OWLeS facilities and field personnel observed six lake-effect thunderstorms during December–January 2013/14. Most of them produced very little lightning (fewer than six cloud-to-ground strokes or intracloud pulses recorded by the National Lightning Detection Network). The 7 January 2014 storm had over 50 strokes and pulses, however, which resulted in 20 flashes over a 6-h period (0630–1230 UTC), making it the most electrically active storm during the field campaign. Relative to the 18 December 2013 storm, which only had three flashes, the 7 January 2014 case had a deeper boundary layer and greater instability. Also, 45% of the lightning during the 7 January storm was likely due to flashes initiated by wind turbines or other man-made antennas, along with all of the lightning observed during 18 December. No lightning was documented over Lake Ontario, the primary source of instability for these storms.

Refusal of cervical spine immobilization after blunt trauma: Implications for initial evaluation and management: A retrospective cohort study

IntroductionRigid cervical collars are routinely placed in the pre-hospital setting after significant blunt trauma. Patients who are deemed competent by field personnel (Glasgow Coma Scale (GCS) ≥13, no major distracting injury and not grossly intoxicated) may refuse cervical collar placement. Material and methodsA retrospective review was conducted of all adult trauma patients presenting to a Level 1 trauma center after blunt trauma with a GCS≥13 and no distracting injury or gross intoxication from January 2014 to December 2014. Pre-hospital data was collected from emergency medical service reports and hospital data from patient charts. Cervical spine injury was identified by International Classification of Disease-9th Revision codes. Patients refusing cervical spine immobilization prior to arrival are compared to those who were compliant. ResultsA total of 629 patients met inclusion criteria. Cervical spine immobilization was refused by 28 patients, while 601 complied. There were 16 cervical spine injuries (2.5%), with 3 (10.7%) in noncompliant patients and 13 (2.2%) among those who were complaint (p = 0.03). ConclusionThe incidence of cervical spine injuries in patients refusing cervical collar immobilization is higher than in compliant patients. Patients arriving for initial evaluation having refused cervical collar immobilization should be treated with caution.

Cybersecurity in Power Systems

Did you know that cyberattackers have already created outages in Ukraine, not once but twice? On 23 December 2015, cyberintruders at three electric distribution companies in Ukraine opened breakers, creating a power outage that affected 225,000 people. Power was restored in approximately 6 h, as field personnel manually closed the breakers.

THE DOSE-RESPONSE HILL SLOPE USED IN THE AQUATIC TOXICITY MODULE OF OIL SPILL MODELS SHOULD BE MUCH STEEPER

ABSTRACT One useful tool for assessing the potential adverse effects of oil spills on aquatic and marine environments are computational models that calculate the transport, fate and effects of substances. The use of computational models is authorized by 43 CFR 11, and has been codified in the Natural Resources Damage Assessment for Coastal and Marine Environments (NRDAM/CME). The code in the NRDAM/CME is incorporated into modules that compute how a spilled substance moves through a marine environment and affects biological resources. This paper describes an apparent error in one module of the NRDAM code, the one that calculates the mortality rate of exposed organisms to concentrations that are less than the medial lethal concentration (LC50) of a mixture of dissolved petroleum hydrocarbons. Based on a review of the original publication, we have concluded the original mean “hill slopes” range from 4.34 to 24.16, which are much greater than the slope (functions) of 1.1 to 1.7 that are used in a module of the NRDAM/CME code. The use of a probit slope function as a logistic slope has the effect of overestimating the mortality rate for marine species exposed to low hydrocarbon concentrations and underestimating mortality of marine species exposed to concentrations greater than the LC50. The “shallow” slope function also calculates a much lower threshold concentration at which adverse effects are expected, than is supported by slope values indicated by the original source. The error in the slope of this module may also explain, in part, why field personnel have not observed fish kills when the NRDAM/CME has predicted mortalities occurred.

Overcoming Challenges and Optimizing Liner Deployment in Long Laterals

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181288, “Overcoming Challenges, Improving Understanding, and Optimizing Liner Deployment in Long Laterals,” by Kim Daniel Mathisen, SPE, Tore Sørheim, SPE, and Tom Rune Koløy, SPE, Trican, and Neil Decker, SPE, Hess, prepared for the 2016 SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. The paper has not been peer reviewed. A North Sea field development included installation of long 4½-in. completion liners in the horizontal reservoir sections of each well. To minimize overall risk, the operator planned to use managed-pressure drilling (MPD). Despite the expected positive effects of MPD, a strong understanding of the downhole dynamics during deployment of the lower-completion liner was needed. To address this challenge, the operator installed a newly developed work-string-dynamics logging tool directly above the liner-hanger-running tools. Logging-Tool Technology Overview The logging tool is a compact and robust memory-based surveillance tool made for drillpipe-conveyed well operations. It is designed, manufactured, and maintainde to be used without being a weak point in the string. The tool is equipped with an electronics package consisting of sensors, batteries, and memory. The logging tool records Axial loads (tension and compression) Torque (left/right) Pressures (absolute internal pressure, absolute external pressure, and differential pressure) Temperature The logging tool is typically shipped to the rigsite ready to be run in the well. The rig crew treats the tool as though it is a standard drillpipe pup joint, eliminating the need for dedicated field personnel and increased personnel on board. After starting logging, the tool can log for 36 days continuously, enabling multiple installations on a single battery package. Logging Tool Description The tool consists of three major components—a main body, housing, and a tool joint. The main body (inner mandrel) contains the electrical package with sensors. The housing protects the electrical package and is installed on the outside of the main body. The tool joint is mounted on the lower section on the main body. Axial loads on the tool are transferred through the main body and the tool joint; the housing is not exposed to axial loads. Torque is transferred through a robust spline system. Results The logging tool was placed directly above the liner-hanger-running tool in the installation string on seven liner deployments in this campaign. For all installations, both the primary logging tool and the backup were started at the base before shipment to the rigsite. This allowed for testing and field verification of the tool’s memory and battery lifetime. After completion-liner installation, the operator recovered the deployment string to surface with the logging-tool memory sub, which was then shipped back to the workshop onshore. The vendor then downloaded the data for analysis.

Guest Editorial: Are You Ready for the Oilfield Recovery?

Guest editorial Through the past 2-plus years, the petroleum industry has experienced its deepest downturn since the late 1980s. It feels like, in the words of baseball legend Yogi Berra, “It’s déja vu all over again.” Like other market cycle downturns, a dramatic drop in the price of oil leading to sharp cuts in investment in exploration and production has affected the entire industry and the people who work in it. Also, as in past downturns, company after company laid off high percentages of their employees across the board and, in many cases, also enacted hiring freezes. Since the steep drop in oil prices, an estimated 250,000 workers directly involved in the petroleum industry have lost their jobs. From field personnel to executives, the toll has been high. Many of these laid off workers have moved on to find employment in other industries, often with more comfortable and predictable working conditions than is the norm in the oil field. It will be difficult for our industry to attract a large percentage of these workers to return. Add to this scenario “the great crew change” created by the retirement of many senior level people who joined the industry in the boom of the late 1970s and early 1980s, and it is conceivable that the industry may be headed for an all-time low in the availability of qualified personnel. Midcareer professionals are in short supply, and few qualified candidates are left to replace the retiring senior management. For the industry, this loss of expertise will be difficult to replace when the next boom comes. At the same time, enrollment in petroleum engineering degree programs across the US is falling, making the talent shortage even more acute. After Bust Comes Recovery If history is any guide, after every oil bust comes a recovery. Currently, there is rising confidence in the industry that crude oil could strengthen and stabilize at higher levels later this year. While the recovery has been slow, the industry is seeing encouraging signs that the rebound has begun. Two indicators are the increase in oil and natural gas drilling and production activity in Oklahoma and in the Permian Basin of west Texas and southeast New Mexico. With activity picking up, the US oil industry will need to hire tens of thousands of new workers as oil prices recover and drillers stand up rigs.

Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC- and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure.

Protocolo estandarizado para el seguimiento poblacional del pavón, Oreophasis derbianus: propuesta de métodos de campo y analíticos

La estimación robusta del tamaño poblacional de especies de fauna silvestre es un requisito fundamental en estudios ecológicos y en el diseño e implementación de estrategias de conservación. Sin embargo, en varios casos, estas estimaciones carecen de exactitud, precisión, representatividad y comparabilidad. Esto ha ocasionado que la información generada para las poblaciones de una determinada especie sea difícilmente generalizable y que su aplicación sea limitada. Éste es el caso del pavón, Oreophasis derbianus, un ave endémica de Mesoamérica y en peligro de extinción. En este trabajo presentamos una propuesta de protocolo de muestreo en campo y métodos analíticos estandarizados para el estudio poblacional de esta especie. La propuesta está basada en nuestra experiencia en el estudio de la especie y en la aplicación de la teoría del muestreo de distancias. El propósito de este protocolo estandarizado es generar datos de campo de calidad para obtener estimaciones robustas de la abundancia poblacional de O. derbianus. Este protocolo lo desarrollamos y evaluamos principalmente en la Reserva de la Biosfera El Triunfo, en Chia­pas, México, pero también fue replicado en otras tres localidades en México y Guatemala. Su desarrollo y evaluación incluyó muestreos mensuales en campo durante casi diez años y la impartición de talleres de capacitación teórico-prácticos a personal de campo para la adecuada implementación de este protocolo. Como resultado de esta experiencia, definimos una serie de li­neamientos básicos y factibles que proponemos sea un estándar como métodos de muestreo y analíticos de las poblaciones de O. derbianus para asegurar la calidad y comparabilidad de los datos generados en campo. Finalmente, presentamos estimaciones de la densidad poblacional de O. derbianus en las cuatro localidades estudiadas y discutimos el significado de su variación espacial y temporal en el contexto de esta propuesta.

Establishing Content Validity of the Quality Indicators for Classrooms Serving Students With Autism Spectrum Disorders Instrument

The purpose of this study was to provide content validation for quality indicators included in an observation instrument developed to evaluate classrooms serving students with autism spectrum disorders (ASD). A panel of 103 subject-matter experts consisting of a mixture of field personnel ( n = 64; 59 classroom teachers, five school administrators) and university faculty ( n = 39) provided feedback regarding construct validity. Results were analyzed using two approaches: a simple aggregation of responses in the form of averages, with an a priori threshold for the determination of what will be considered acceptable, and Lawshe’s Content Validity Ratio methodology. All items in the instrument were deemed to have demonstrated content validity as did the overall instrument. Based on the data from this study, the Quality Indicators for Classrooms Serving Students With ASD (QIASD) instrument can be considered to have demonstrated content validity.