A species- and traits-based approach to predictive mapping of the distribution and diversity of costal benthic assemblages
Bibliographic record
Abstract
Maps of seafloor habitats are important for managing marine areas as they delineate distinct regions of the seabed based on their bio-physical characteristics. Spatially continuous sonar-derived bathymetry and backscatter data, and derivative terrain and textural features are used to predict the distribution of species and communities. Various technical and conceptual methods have been recommended to develop more accurate and informative habitat maps. In support of current literature recognizing the importance of scale in determining species occurrence, Chapter 2 recommends a feature selection method for studies using multiple scales to calculate geomorphic features. Based on this information, full-coverage habitat maps of species assemblages across two coastal sites are predicted. Additionally, Chapter 3 proposes biological traits analysis (BTA) to assess the functional composition of species assemblages, and models continuous maps depicting the spatial distribution of taxonomic and functional diversity metrics. Since current methods to develop habitat maps mainly use a taxonomic approach based on species community composition, a functional traits-based approach assessing a species’ behaviour, life-history, and morphology provides a stronger link to broader ecosystem functions for the region. Together, these results are complimentary and provide spatially explicit management tools to support evidence-based decision-making in a changing marine environment.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".