Norfish: Spanish and Basque Newfoundland Cod Fishery 1520-1790
Bibliographic record
Abstract
NorFish is a European Research Council (ERC) Advanced Grant led by Prof Poul Holm in Trinity College Dublin, focuses on the premise that a 16th century shift in marine fish pricing and supply in conjunction with the Little Ice Age and lowering of sea temperatures not only rise to the North Atlantic Fish Revolution but also forms one of the first documented examples of the disrupting effects of globalisation and climate change. The project examines the role of the Fish Revolution for a range of inter-related aspects of North Atlantic history, with NorFish’s interdisciplinary team drawing on archaeology, history, cartography, geography, and ecology to develop interpretative frameworks that synthesise a broad spectrum of source data to assess the overall objective of the project. NorFish’s interdisciplinary team draws on archaeology, history, cartography, geography, and ecology to assess the objectives of the project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".