Modeling spatiotemporal variabilities of length-at-age growth characteristics for slow-growing subarctic populations of Lake Whitefish, using hierarchical Bayesian statistics
Bibliographic record
Abstract
Though Lake Whitefish are ecologically, culturally and economically important to aboriginal communities in the Northwest Territories, Canada, growth characteristics of the fish populations have not received extensive interpretations, resulting in a lack of quantitative information to support fisheries management efforts in subarctic great lake systems. The overall objective of this study is to investigate spatiotemporal variations of growth characteristics of Lake Whitefish populations in Great Slave Lake (GSL) from 1972–2009. Using hierarchical Bayesian statistics , we structured four candidate growth models: generalized (GGM), logistic (LGM), Gompertz (PGM), and von Bertalanffy (VBM), with four parameterization scenarios combining all possible options of varying or constant L ∞ and K . In terms of deviance information criterion (DIC) and multimodel inference (MMI), the plausibility of the candidate models was evaluated to select the best combinations of growth models and the parameter scenarios. The GGM with varying L ∞ and K best delineated the fish growth characteristics in almost all areas of GSL, while the fish growth model parameterized with constant L ∞ and varying K performed best in the shallow western basin. The VGM where L ∞ and K were varied partially described fish growth in the shallow waters. Applying the MMI-based growth analysis, we found that smaller and slower-growing fish were mainly distributed in deep waters, while larger and faster-growing fish inhabited shallow waters. These spatiotemporal variations of fish growth characteristics have been attributed to the presence of coupled impacts derived from both climate-driven and anthropogenic events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".