Ecological Forecasts Reveal Limitations of Common Model Selection Methods: Predicting Changes in Beaver Colony Densities
Bibliographic record
Abstract
As furbearers and ecosystem engineers, predicting beaver densities has important economic and ecological implications. We evaluated whether model selection using information criteria would retain the most parsimonious model with the greatest accuracy to predict beaver colony density fluctuations. Although information criteria favored the performance of complex models, empirical validations of density predictions revealed simple models forecasted densities nearly as well. Our results suggested density-dependent mechanisms were a main driver of beaver colony density fluctuations. Our study demonstrated the importance of validating model predictions and revealed how a limitation of information criteria (over-fitting complex models) can affect interpretations of ecological dynamics. These photographs illustrate the article “Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities” by Sean M. Johnson-Bice, Jake M. Ferguson, John D. Erb, Thomas D. Gable, Steve K. Windels published in Ecological Applications. https://doi.org/10.1002/eap.2198
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".