Time-travelling with Atlantic cod: A modelling approach to historical distribution in Newfoundland
Bibliographic record
Abstract
This paper presents preliminary results on how biolo- gy, ecosystems, and fisheries have shaped the historical (1815–1992) distribution of Atlantic cod (Gadus morhua) in Newfoundland (Canada). The work has been devel- oped within the scope of the “Fishing Architecture” re- search project, which explores the historical continuum between fish and coastal architecture. This relationship is believed to be modulated by the morpho- and eco-physiological characteristics of fish. Our approach involves developing, calibrating, and validating a habitat-suitable model, using Maximum Entropy (MaxEnt). This model computed the maxi- mum entropy distribution of abiotic (bathymetry, wa- ter temperature, salinity, nutrients) and biotic (phyto- plankton, prey distribution) descriptors over the binary data (presence/absence) of cod, gathered from publicly available databases. Afterwards, we mapped the most suitable areas for cod presence. To assess the model’s performance and uncertainties, we ran a global sensi- tivity analysis, using the GSAT package. The model results were further explored by overlaying the logbook routes of Portuguese cod-fishing vessels to Newfoundland (discrete data between 1840–1950), to understand the relative impact of this overseas fish- ery. The research complements previous approaches to understanding the breakdown of the cod population and provides a tool to assess the relationship between terrestrial constructions to support fisheries and marine fish populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".