Relationships between Cold-water Corals off Newfoundland and Labrador and their Environment
Bibliographic record
Abstract
Cold-water corals inhabit deep-water environments in many parts of the world, but the factors governing their distribution remain unclear. Effective cold-water coral conservation strategies depend on knowledge of their distributions, their ecology, and the threats they face. The main objective of this study is to quantify relationships between corals occurrence/abundance and six environmental parameters to determine how these parameters may control the distribution of cold-water coral species in the Northwest Atlantic region. Data for six environmental parameters and two types of coral bycatch data were obtained. These datasets were processed to obtain regularly gridded data of each parameter which were subsequently sampled at each recorded coral location to build a sufficient database for analysis. The Geographically Weighted Regression (GWR) technique, along with correlation analysis, was used. This research approach is beneficial, as other similar studies have not accounted for local or spatial variation in the relationships between coral species and environmental parameters as using GWR allows this study to do. The strongest associations between environmental parameters and coral were used to determine which variables to use in the GWR analysis. Other strong associations between
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".