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The Importance of Lagrangian Water Properties for the Abundance Estimation of Walleye Pollock (Gadus Chalcogrammus) in the Okhotsk Sea

https://doi.org/10.2139/ssrn.4671587
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inferredcites →https://doi.org/10.1002/eap.1610openalex-extracted
Fit to predict? Eco‐informatics for predicting the catchability of a pelagic fish in near real time
inferredcites →https://doi.org/10.1007/978-3-319-53022-2openalex-extracted
Lagrangian Oceanography
inferredcites →https://doi.org/10.1016/j.fishres.2004.08.002openalex-extracted
Standardizing catch and effort data: a review of recent approaches
inferredcites →https://doi.org/10.1016/j.fishres.2004.08.011openalex-extracted
GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research
inferredcites →https://doi.org/10.1016/j.fishres.2004.08.029openalex-extracted
Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet
inferredcites →https://doi.org/10.1016/j.fishres.2023.106642openalex-extracted
Those who fail to learn from history are condemned to repeat it: A perspective on current stock assessment good practices and the consequences of not following them
inferredcites →https://doi.org/10.1016/j.pocean.2022.102790openalex-extracted
Marine life at Lagrangian fronts
inferredcites →https://doi.org/10.1080/10618600.2021.2007935openalex-extracted
Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models
inferredcites →https://doi.org/10.1093/icesjms/fsab104openalex-extracted
Validation of stock assessment methods: is it me or my model talking?
inferredcites →https://doi.org/10.1093/icesjms/fsr174openalex-extracted
Influence plots and metrics: tools for better understanding fisheries catch-per-unit-effort standardizations
inferredcites →https://doi.org/10.1111/1365-2664.12330openalex-extracted
REVIEW: On the Front Line: frontal zones as priority at‐sea conservation areas for mobile marine vertebrates
inferredcites →https://doi.org/10.1111/1467-9868.00374openalex-extracted
Thin Plate Regression Splines
inferredcites →https://doi.org/10.1111/faf.12427openalex-extracted
Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model
inferredcites →https://doi.org/10.1111/j.1467-9868.2010.00749.xopenalex-extracted
Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
inferredcites →https://doi.org/10.1134/s0001433822120088openalex-extracted
Lagrangian Analysis of Pacific Waters in the Sea of Okhotsk Based on Satellite Data in Application to the Walleye Pollock Fishery
inferredcites →https://doi.org/10.1175/2007jcli1824.1openalex-extracted
Daily High-Resolution-Blended Analyses for Sea Surface Temperature
inferredcites →https://doi.org/10.1198/016214504000000980openalex-extracted
Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
inferredcites →https://doi.org/10.18637/jss.v077.i01openalex-extracted
ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
inferredcites →https://doi.org/10.21105/joss.01903openalex-extracted
mlr3: A modern object-oriented machine learning framework in R
inferredcites →https://doi.org/10.2307/1270747openalex-extracted
The Theory of Dispersion Models
inferredcites →https://doi.org/10.2307/2290538openalex-extracted
Generalized Additive Models.
inferredcites →https://doi.org/10.32614/r.manualsopenalex-extracted
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.36038/2307-3497-2022-187-71-86openalex-extracted
Condition and structure of pollock stock in the Sea of Okhotsk in 2011–2020
inferredcites →https://doi.org/10.36038/2307-3497-2022-189-180-197openalex-extracted
Population structure of walleye pollock within the Asian part of the range based on the analysis of various genetic markers
inferredcites →https://doi.org/10.6084/m9.figshare.17086871openalex-extracted
Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models
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