A spatial–temporal approach to modeling somatic growth across inland recreational fisheries landscapes
Bibliographic record
Abstract
We develop a mechanistically motivated von Bertalanffy growth model to estimate growth rate and its predictors from spatial–temporal data and compare this model’s performance with a suite of commonly used mixed-effects growth models. We test these models with simulated data and then apply them to test whether concerns that high density is causing growth suppression of walleye (Sander vitreus) in Alberta, Canada, are supported using data collected during 2000–2017. Simulation experiments demonstrated that models that failed to account for complex dependency structures often resulted in growth rate estimates that were less accurate and biased low as judged by median absolute relative error and median relative error, respectively. The magnitude of this bias depended on the parameter values used for simulation. For the case study, a spatial–temporal model was more parsimonious and had higher predictive performance relative to simpler models and did not support the slow-growing walleye hypothesis in Alberta. These findings demonstrate the importance of considering spatial–temporal correlation in analyses that rely on surveillance-style monitoring datasets, particularly when examining relationships between life-history traits and environmental characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".