Exploring drivers of capelin (Mallotus villosus) and Atlantic cod (Gadus morhua) population dynamics using Empirical Dynamic Modelling (EDM)
Bibliographic record
Abstract
Capelin (Mallotus villosus) populations on the Newfoundland shelf collapsed in the early 1990s, coinciding with an ecosystem regime shift and greatly reduced capelin biomass which both persist to this day. The dual-regime nature of this stock’s history suggests it may experience nonlinear dynamics, which are difficult to predict using linear models. This thesis explores the application of nonlinear Empirical Dynamic Modelling (EDM) forecasting tools to capelin biomass data, seeking to determine if capelin dynamics are nonlinear, if nonlinear predictive models of capelin population dynamics outperform linear models, what climatic and ecological factors drive nonlinear changes in capelin biomass, and if these driving forces can be measured and compared. In my first chapter, I found capelin dynamics were nonlinear, and EDM predictive models returned equal or improved model diagnostics to linear models in most situations. In my second chapter, I identified a strong positive association between capelin and Atlantic cod dynamics, with both species being driven by long term climatic change and likely to benefit from mild warming. This thesis clearly identifies the utilities of EDM as a tool for use in stock assessment in detecting and forecasting nonlinear stock dynamics, and identifying and characterizing factors driving population dynamics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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 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".