Low Residual Error Research Articles

CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems.

Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.

The heterogeneous Aquila optimization algorithm.

A new swarm-based optimization algorithm called the Aquila optimizer (AO) was just proposed recently with promising better performance. However, as reported by the proposer, it almost remains unchanged for almost half of the convergence curves at the latter iterations. Considering the better performance and the lazy latter convergence rates of the AO algorithm in optimization, the multiple updating principle is introduced and the heterogeneous AO called HAO is proposed in this paper. Simulation experiments were carried out on both unimodal and multimodal benchmark functions, and comparison with other capable algorithms were also made, most of the results confirmed the better performance with better intensification and diversification capabilities, fast convergence rate, low residual errors, strong scalabilities, and convinced verification results. Further application in optimizing three benchmark real-world engineering problems were also carried out, the overall better performance in optimizing was confirmed without any other equations introduced for improvement.

Development of a Control Oriented Scramjet Engine Inlet Model

Controller design for a realistic scramjet (Supersonic Combustion Ramjet) model is an extremely computationally intense process. To mimic the real-time scramjet performance, several researchers have developed linear models, nonlinear models, computational fluid dynamic (CFD) based models, etc. The linear models or nonlinear models based on ordinary differential equations are not sufficient to capture the complete dynamics of the scramjet; whereas CFD based models are highly computationally intense - which may vary from hours to days to complete one full closed-loop simulation of a realistic test case. The scramjet engine model can be divided in to three parts; inlet, combustor and exhaust. In this paper, the key focus is to develop the inlet model from the CFD simulations for various test cases and then capture vital scramjet engine parameters, which are later required for controller design for the scramjet engine. The models developed from the captured vital scramjet parameters has two advantages; high fidelity model and are less computationally intensive. The simulations are performed for various test cases at operating Mach numbers of 7, 6.5 and 6, angle of attack of 0 deg, ±1 deg, and ±2 deg, and various cowl angles. Control oriented polynomial inlet models are developed from the CFD data for all the operating points, the accuracy of the developed polynomial models is observed in terms of low residual errors.

Optimization Algorithm of Effective Stress Coefficient for Permeability

The effective stress coefficient for permeability is a significant index for characterizing the variation in permeability with effective stress. The realization of its accuracy is essential for studying the stress sensitivity of oil and gas reservoirs. The determination of the effective stress coefficient for permeability can be mainly evaluated using the cross-plotting or response surface method. Both methods preprocess experimental data and preset a specific function relation, resulting in deviation in the calculation results. To improve the calculation accuracy of the effective stress coefficient for permeability, a 3D surface fitting calculation method was proposed according to the linear effective stress law and continuity hypothesis. The statistical parameters of the aforementioned three methods were compared, and the results showed that the three-dimensional (3D) surface fitting method had the advantages of a high correlation coefficient, low root mean square error, and low residual error. The principal of using the 3D surface fitting method to calculate the effective stress coefficient of permeability was to evaluate the influence of two independent variables on a dependent variable by means of a 3D nonlinear regression. Therefore, the method could be applied to studying the relationship between other physical properties and effective stress.

Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.

A novel grid regression demodulation method for radiographic grid artifact correction.

In x-ray radiography, the commonly used antiscatter grid for enhancing image quality causes artifacts in the form of periodic noises, such as shadows, cutoff, and Moiré fringes. Software degridding is traditionally performed via linear or homomorphic filtering in the spectral domain. These methods inevitably result in image blurring, information loss, and distortion, thus hindering detection and assessment of diseases. We seek effective and practical solutions for grid artifact correction based on spatial-domain analysis toward high-quality imaging. By analyzing the physical process of grid artifact formation, we track down the root of the problem associated with spectral filtering. We propose the grid regression demodulation (GRD). The degridding cost is forged as a functional of the latent x-ray photon image and parametric grid model characterizing grid transmission property. Regularization on the grid spectra is incorporated. We devise optimization algorithms for artifact correction and grid pattern estimation. GRD decouples the partially overlapped spectra of the grid and anatomy, and removes the artifacts independently, thus restoring the underlying clinically relevant data. Method efficacy is demonstrated using simulated and real data. GRD effectively preserves image edges, textures, and patterns while removing grid artifacts. For the known ground truth setting, GRD gives a near-perfect correction. For real data, GRD is capable of correcting not only the primary grid artifacts, but also the higher grid harmonic artifacts while keeping image content unaltered, which is unachievable by the other methods. Our method has low residual errors and exhibits a successful demodulation effect without introducing additional artifacts, while ringing or cilia artifacts are present in the others. The proposed method outperforms the prevalent transform techniques for correcting grid artifacts in digital radiography. It is self-sustained and self-adaptive to a range of targets and beam quality. Our approach is advantageous in restoring the latent image while suppressing grid noises. It retrieves the true scale factor of the degridded data, which is unattainable via any spectral filtering techniques. This work unlocks a promising venue to improve and upgrade low-dose medical radiographic imaging technology.

Designing potent inhibitors against the multidrug resistance P-glycoprotein

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates’ QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction. Communicated by Ramaswamy H. Sarma

Reduced complexity hard‐ and soft‐input BCH decoding with applications in concatenated codes

Error correction coding for optical communication and storage requires high rate codes that enable high data throughput and low residual errors. Recently, different concatenated coding schemes were proposed that are based on binary BCH codes with low error correcting capabilities. In this work, low-complexity hard- and soft-input decoding methods for such codes are investigated. We propose three concepts to reduce the complexity of the decoder. For the algebraic decoding we demonstrate that Peterson's algorithm can be more efficient than the Berlekamp–Massey algorithm for single, double, and triple error correcting BCH codes. We propose an inversion-less version of Peterson's algorithm and a corresponding decoding architecture. Furthermore, we propose a decoding approach that combines algebraic hard-input decoding with soft-input bit-flipping decoding. An acceptance criterion is utilised to determine the reliability of the estimated codewords. For many received codewords the stopping criterion indicates that the hard-decoding result is sufficiently reliable, and the costly soft-input decoding can be omitted. To reduce the memory size for the soft-values, we propose a bit-flipping decoder that stores only the positions and soft values of a small number of code symbols. This method significantly reduces the memory requirements and has little adverse effect on the decoding performance.

Efficient Communication Protection of Many-Core Systems against Active Attackers

Many-core system-on-chips, together with their established communication infrastructures, Networks-on-Chip (NoC), are growing in complexity, which encourages the integration of third-party components to simplify and accelerate production processes. However, this also adversely exposes the surface for attacks through the injection of hardware Trojans. This work addresses active attacks on NoCs and focuses on the integrity and availability of transmitted data. In particular, we consider the modification and/or dropping of data during transmission as active attacks that might be performed by malicious routers. To mitigate the impact of such active attacks, we propose two lightweight solutions that respect the performance constraints of NoCs. Assuming the presence of symmetric keys, these approaches combine lightweight authentication codes for integrity protection with network coding for increased efficiency and robustness. The proposed solutions prevent undetected modifications and significantly increase availability through a reliable detection of attacks. The efficiency of these solutions is investigated in different scenarios using cycle-accurate simulations and the area overhead is analyzed relative to state-of-the-art many-core system. The results demonstrate that one authentication scheme with network coding protects the integrity of data to a low residual error of 1.36% at 0.2 attack probability with an area overhead of 2.68%. For faster and more flexible evaluation, an analytical approach is developed which is validated against the cycle-accurate simulations. The analytical approach is more than 1000× faster while having a maximum estimation error of 5%. Moreover, the analytical model provides a deeper insight into the system’s behavior. For example, it reveals which factors influence the performance parameters.

Estimating soil-water characteristic curve based on soil type and best-fitting regressions derived from a simplified method using Aburra Valley dataset

In unsaturated soil mechanics, many attempts have been made to estimate the SWCC based on soil texture and grain-size distribution. This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse and fine-grained soils using SWCC data and machine learning computer code in the Aburra Valley. Fredlund and Xing parameters has been used to estimate the SWCC correlations. Soil samples collected from field survey were subjected to laboratory testing, SWCCs were estimated using filter paper method. Each SWCC data set from Aburra Valley was fitted with Fredlund and Xing curve using multiple regression analysis, correlations were derived for those four parameters based on predictors derived from machine learning. The proposed method gives a good estimation and low residual errors of the SWCC.

Tolerance to Residual Refractive Errors After Trifocal and Trifocal Toric Intraocular Lens Implantation.

The objective of this study was to assess the impact of 0.50 diopter (D) positive or negative defocus on visual function in patients implanted with trifocal and trifocal toric intraocular lenses (IOLs). The study included patients implanted with the AcrySof IQ PanOptix IOL or the PanOptix Toric. Visual acuity (VA) at high (100%), medium (50%) and low (10%) contrast, contrast sensitivity function (CSF), and halo perception were assessed three months after surgery. Explorations were performed with corrected distance visual acuity (CDVA), with a positive defocus of +0.50D (myopization) and with a negative defocus of -0.50D (hyperopization). The study included 60 eyes of 60 patients (30 eyes with PanOptix and 30 eyes with PanOptix Toric). For both groups, VA was better for all contrast settings at the CDVA situation (P<0.05 in all cases). For low spatial frequencies, no differences in CSF were found among the three refractive situations in either group. For higher frequencies, the results showed an overall trend for better CSF results for the CDVA situation. The halo effect was lower for the CDVA situation if compared to myopization and hyperopization in both the PanOptix and the PanOptix Toric groups (P<0.05 for all cases). There is an impact on visual quality and halo perception in patients implanted with trifocal or trifocal toric IOLs even for low residual refractive errors.

The improved equilibrium optimization algorithm with multiple updating discipline

In this paper, a hybridization algorithm of the equilibrium optimization (EO) algorithm and the slime mould (SM) algorithm was proposed. The better performance of the SM algorithm in optimization both benchmark functions and real engineering problems were all encouraging. And consequently, the multiple updating discipline for individuals in swarms was introduced to the EO algorithm. Simulation experiments were carried out and the faster convergence, lower residual errors, higher accuracy verified the improved EO algorithm would perform better than the original one.

Population Pharmacokinetics of Remimazolam in Procedural Sedation With Nonhomogeneously Mixed Arterial and Venous Concentrations

Remimazolam is an ultra‐short acting benzodiazepine under development for procedural sedation and general anesthesia. Population pharmacokinetic analysis (PopPK) was conducted for remimazolam with arterial and venous samples previously, but results were limited by arterial‐venous concentration differences and inaccurate central volume of distribution (V1) estimates. A new model was developed to describe covariate effects after accounting for arterial‐venous differences. Arterial and venous plasma concentration‐time data from 11 clinical trials were pooled for PopPK. Data from two constant‐rate infusion studies were used to account for venous‐to‐arterial (VtoA) ratio within residual error and to accurately estimate V1. V1 and VtoA ratio from the pilot model were applied to the full dataset, where the optimal fixed/random effects and covariates were assessed. VtoA ratio was described using a maximum effect (Emax) model during infusion and as a constant postdose. V1 was estimated as 4.83 L for a 70 kg subject and interindividual variability (IIV) on V1 could only be estimated in studies with early concentrations. IIV on clearance was low (22.9%). Covariates included effects of sex on clearance (women 10% > men), and race on clearance and steady‐state volume of distribution (African Americans 16% < other races). Arterial‐venous concentration differences were best described using an Emax model during infusion with a constant ratio after infusion, resulting in low residual error (20.7%). There are no clinically relevant dose adjustments needed for any covariates based on pharmacokinetic differences.

New technique for automatic sonographic measurement of change in head-perineum distance and angle of progression during active phase of second stage of labor.

To evaluate the performance of a new ultrasound technique for the automatic assessment of the change in head-perineum distance (delta-HPD) and angle of progression (delta-AoP) during the active phase of the second stage of labor. This was a prospective observational cohort study including singleton term pregnancies with fetuses in cephalic presentation during the active phase of the second stage of labor. In each patient, two videoclips of 10 s each were acquired transperineally, one in the axial and one in the sagittal plane, between rest and the acme of an expulsive effort, in order to measure HPD and AoP, respectively. The videoclips were processed offline and the difference between the acme of the pushing effort and rest in HPD (delta-HPD) and AoP (delta-AoP) was calculated, first manually by an experienced sonographer and then using a new automatic technique. The reliability of the automatic algorithm was evaluated by comparing the automatic measurements with those obtained manually, which was considered as the reference gold standard. Overall, 27 women were included. A significant correlation was observed between the measurements obtained by the automatic and the manual methods for both delta-HPD (intraclass correlation coefficient (ICC) = 0.97) and delta-AoP (ICC = 0.99). The high accuracy provided by the automatic algorithm was confirmed by the high values of the coefficient of determination (r2 = 0.98 for both delta-HPD and delta-AoP) and the low residual errors (root mean square error = 1.2 mm for delta-HPD and 1.5° for delta-AoP). A Bland-Altman analysis showed a mean difference of 0.52 mm (limits of agreement, -1.58 to 2.62 mm) for delta-HPD (P = 0.034) and 0.35° (limits of agreement, -2.54 to 3.09°) for delta-AoP (P = 0.39) between the manual and automatic measurements. The automatic assessment of delta-AoP and delta-HPD during maternal pushing efforts is feasible. The automatic measurement of delta-AoP appears to be reliable when compared with the gold standard manual measurement by an experienced operator. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Semimechanistic Modeling of Eravacycline Pharmacodynamics Using In Vitro Time-Kill Data with MIC Incorporated in an Adaptive Resistance Function.

Effective bacterial infection eradication requires not only potent antibacterial agents but also proper dosing strategies. Current practices generally utilize point estimates of the effects of therapeutic agents, even though the actual kinetics of exposure are much more complex and relevant. Here, we use a full time course of the observed in vitro effects to develop a semimechanistic pharmacokinetic-pharmacodynamic model for eravacycline against multiple Gram-negative bacterial pathogens. This model incorporates components such as pharmacokinetics, bacterial life cycle, and drug effects to quantitatively describe the time course of antibacterial killing and the emergence of resistance. Model discrimination was performed by comparing goodness of fit, convergence diagnostics, and objective function values. Models were validated by assessing their abilities to describe bacterial count time courses in visual predictive checks. The final model describes 576 bacterial counts (expressed in log10 CFU per milliliter) from 144 in vitro time-kill experiments with low residual error and high precision. We characterize antibacterial susceptibility as a function of the MIC and adaptive resistance. In doing so, we show that the MIC is proportional to initial susceptibility at 0 h and the development of resistance over the course of 16 h. Altogether, this model may be useful in supporting dose selection, since it incorporates in vitro pharmacodynamics and clinically observed individual drug susceptibilities.

Automatic measurement of head-perineum distance during intrapartum ultrasound: description of the technique and preliminary results

Objectives To evaluate the accuracy and reliability of a new ultrasound technique for the automatic assessment of the head-perineum distance (HPD) during childbirth. Methods HPD was measured on a total of 40 acquisition sessions in 30 laboring women both automatically by an innovative algorithm and manually by trained sonographers, assumed as gold standard. Results A significant correlation was found between manual and automatic measurements (Intra-CC = 0.994). High values of the coefficient of determination (r 2=0.98) and low residual errors: RMSE = 2.01 mm (4.9%) were found. Conclusion The automatic algorithm for the assessment of the HPD represents a reliable technique.

Analysis of Phenolic Content in Grape Seeds and Skins by Means of a Bio-Electronic Tongue.

A bio-electronic tongue has been developed to evaluate the phenolic content of grape residues (seeds and skins) in a fast and easy way with industrial use in mind. A voltammetric electronic tongue has been designed based on carbon resin electrodes modified with tyrosinase combined with electron mediators. The presence of the phenoloxydase promotes the selectivity and specificity towards phenols. The results of multivariate analysis allowed discriminating seeds and skins according to their polyphenolic content. Partial least squares (PLS) has been used to establish regression models with parameters related to phenolic content measured by spectroscopic methods i.e., total poliphenol content (TPC) and Folin–Ciocalteu (FC) indexes. It has been shown that electronic tongue can be successfully used to predict parameters of interest with high correlation coefficients (higher than 0.99 in both calibration and prediction) and low residual errors. These values can even be improved using genetic algorithms for multivalent analysis. In this way, a fast and simple tool is available for the evaluation of these values. This advantage may be due to the fact that the electrochemical signals are directly related to the phenolic content.

Optimization of titanium dioxide decorated by graphene quantum dot as a light scatterer for enhanced dye-sensitized solar cell performance

Titanium dioxide (TiO 2 ) as a photoanode in dye-sensitized solar cells (DSSCs) has some drawbacks that reduce its photovoltaic performances i.e. low dye loading capacity and low light-harvesting efficiency. Therefore, TiO 2 decorated by graphene quantum dot (GQD) as a light scatterer has been successfully fabricated via electrodeposition and drop-casting. The response surface methodology/central composite design was successfully utilized to optimize the preparation of photoanode with TiO 2 -GQD as a light scattering layer (LSL). A reduced quadratic model was successfully designed to predict the power conversion efficiency (PCE) accurately up to 97% with a 3% residual standard error. The TiO 2 -GQD LSL depicted a cluster of spherical nanoparticles on top of the photoanode that not only enhanced the light scattering effect but also improved the light-harvesting range from visible light to ultraviolet and near-infrared range. The resultant TiO 2 nanoparticles with TiO 2 -GQD LSL showed vast enhancement of PCE up to 66% from 3.06% to 5.01% due to a good synergistic effect. • TiO 2 -GQD as light scattering layer (LSL) was optimized using response surface methodology • The reduced quadratic model displays a low residual standard error. • TiO 2 nanoparticles with TiO 2 -GQD LSL displays enhanced efficiency up to 66%.

Tensor Least Angle Regression for Sparse Representations of Multidimensional Signals.

Sparse signal representations have gained much interest recently in both signal processing and statistical communities. Compared to orthogonal matching pursuit (OMP) and basis pursuit, which solve the and constrained sparse least-squares problems, respectively, least angle regression (LARS) is a computationally efficient method to solve both problems for all critical values of the regularization parameter . However, all of these methods are not suitable for solving large multidimensional sparse least-squares problems, as they would require extensive computational power and memory. An earlier generalization of OMP, known as Kronecker-OMP, was developed to solve the problem for large multidimensional sparse least-squares problems. However, its memory usage and computation time increase quickly with the number of problem dimensions and iterations. In this letter, we develop a generalization of LARS, tensor least angle regression (T-LARS) that could efficiently solve either large or large constrained multidimensional, sparse, least-squares problems (underdetermined or overdetermined) for all critical values of the regularization parameter and with lower computational complexity and memory usage than Kronecker-OMP. To demonstrate the validity and performance of our T-LARS algorithm, we used it to successfully obtain different sparse representations of two relatively large 3D brain images, using fixed and learned separable overcomplete dictionaries, by solving both and constrained sparse least-squares problems. Our numerical experiments demonstrate that our T-LARS algorithm is significantly faster (46 to 70 times) than Kronecker-OMP in obtaining -sparse solutions for multilinear leastsquares problems. However, the -sparse solutions obtained using Kronecker-OMP always have a slightly lower residual error (1.55% to 2.25%) than ones obtained by T-LARS. Therefore, T-LARS could be an important tool for numerous multidimensional biomedical signal processing applications.

Landscape and biophysical controls of lake productivity to inform evaluation of sockeye salmon (Oncorhynchus nerka) populations in data‐limited regions

AbstractLandscape models are increasingly used to classify and predict the structure and productivity of data‐limited aquatic ecosystems. One such suite of ecosystems is on the remote North and Central Coast (NCC) of British Columbia, where sockeye salmon (Oncorhynchus nerka) rear in more than 150 lakes. Given their remoteness and limited resources for assessment, limnological and population monitoring in many of these lakes has been periodic or absent, limiting understanding of the status of populations and their habitats. Lake photosynthetic rate (PR) estimates are foundational to models of sockeye salmon nursery lake productive capacity. Using data from 61 lakes across the NCC, we compared a suite of landscape and lake variables in an information theoretic framework producing a set of models relating these characteristics to lake PR. A categorical variable related to lake biogeochemistry—whether a lake is humic stained, clear, or glacially turbid—was the most important variable predicting lake PR and was included in all models. Lake surface area relative to upstream catchment size and lake perimeter‐to‐surface‐area ratio were also important, with smaller upstream catchments yielding higher production, and high shoreline complexity correlated with lower productivity as measured by limnetic PR. Model‐averaged predictions of PR from the four models with the lowest residual error were created for 96 lakes currently lacking limnological assessments. These landscape models represent a valuable starting point for evaluating lake‐specific carrying capacities for data‐poor sockeye salmon populations under Canada's Wild Salmon Policy.