Trait-based approach on deep-sea corals in the high-seas of the Flemish Cap and Flemish Pass (northwest Atlantic)
Bibliographic record
Abstract
Deep-sea coral aggregations represent important ecosystems in the Flemish Cap, where some areas have been closed to bottom fisheries by the Northwest Atlantic Fisheries Organization in order to implement conservation and management measures and prevent significant adverse impacts on Vulnerable Marine Ecosystems (VMEs). Flemish Cap is an offshore Bank located about 600 km to the east of Newfoundland, separated from the Grand Banks by the Flemish Pass, a channel approximately 1200 m deep. 40 taxa of corals were identified in the area from bottom trawl research vessel surveys (2006-2015) and rock dredges (2009-2010). They included 22 Alcyonacea, 11 Pennatulacea, 3 Antipatharia and 4 Scleractinia. Corals identified were classified according to their biological traits in an attempt to better understand their function in the ecosystem. This study analyzes which biological traits were useful to classify corals in the Flemish Cap and Flemish Pass prioritizing traits where information is available and that capture variation for a range of biological or ecological processes. This study represents a baseline classification, quantifying trait variation among species, for further studies such as habitat suitability models or impact studies from trawl fisheries and oil and gas exploitation, which represent the main potential human activities in the area.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".