Species Distribution Models for sea pen corals in the Flemish Cap and Flemish Pass Area (NorthwestAtlantic Ocean)
Bibliographic record
Abstract
Species Distribution Models (SDMs) are widely used to identify species-environmentrelationships and predicting species occurrence and/or density at un-sampled locations.The SDMs implementation allows describing species geographical trends, toidentify spatial ontogenetic shifts of commercially exploited species and to assessthe effect of climate change on species distribution. Moreover, SDMs could bean essential tool to support the marine spatial planning framework providingessential and easy-to-use interpretation tools, such as predictive distributionmaps, with the final aim of improving management and conservation especially ofvulnerable species as sea pen corals. In this study, a 10-yr period (2007-2017) of a bottom trawl survey was used to estimateand predict the suitability habitat of sea pen species as a function of several environmental variables (i.e. bathymetry, sea bottom temperature, sea bottom salinity, slope, rugosity, aspectof the seabed, etc) in Flemish Cap and Flemish Pass (ATLAS Case Study No 11) using different SDM algorithms. Resultsshow that species exhibit specific habitat preferences and spatial patterns inresponse to environmental variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".