Bibliographic record
Abstract
The Azores Archipelago consists of a small shelf surrounded by a large component of deep oceanic waters dotted with seamounts. The present model is structured by depth and constitutes a first step in applying the Ecopath modeling approach to Atlantic seamounts. It is the result of a collaborative effort with several researchers of the University of the Azores. The model is composed of 43 functional groups including 26 groups of fish classified according to their size and their preferred depth range. Suggestions for future developments are presented. INTRODUCTION The Azores archipelago is a group of nine volcanic islands that are parts of the Mid-Atlantic ridge (Figure 1). The islands and the contiguous shallower waters (< 500 m depth) have an estimated area of 412 km2, only 0.4% of the EEZ area of about one million km2, while seamounts (< 500 m depth) account for another 0.3% (Isidro, 1996). The present model considers only the area that is being exploited by Azorean fishers, 584,000 km-2, i.e., slightly more than half the EEZ. We assumed an annual average water temperature of 19EC (range: 16-22EC). The present model is the product of a collaboration with many scientists of the University of the Azores who shared their knowledge of the ecosystem with the two researchers in charge of constructing the model. (The collaborating researchers are mentioned under the title of the functional group they helped with.) 36° 34° 32° 30° 28° 26°
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".