Species Distribution Modelling and Kernel Density Analysis of Benthic Ecologically and Biologically Significant Areas (EBSAs) and Other Benthic Fauna in the Maritimes Region
Bibliographic record
Abstract
We present input data and output maps for random forest species distribution models and kernel density estimation (KDE)-derived significant areas for benthic Ecologically and Biologically Significant Areas (EBSAs) and other benthic taxa in Fisheries and Oceans Canada’s Maritimes Region: Horse Mussel (Modiolus modiolus) Beds, Stalked Tunicate Fields, Sand Dollar Beds, Soft Coral Gardens, and Flabellum cup corals. Using a suite of sixty-six environmental variables derived from various sources and native spatial resolutions (available at DOI: 10.17632/34hhtjyyd3.1), random forest was employed to predict the probability of occurrence and biomass distribution of these taxa using data collected from DFO multispecies trawl surveys, DFO scallop stock assessment surveys, and targeted in situ benthic camera and/or video surveys. Random forest presence-absence models had excellent predictive capacity, with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.868 to 0.965. Significant concentrations (as defined by KDE) of these taxa are considered conservation priorities in the Scotian Shelf Bioregional Marine Protected Area (MPA) Network Design Strategy, and are currently being used in exploratory conservation planning analyses. Data include KDE polygons and associated position of trawl sets used to delineate the polygons, presence/absence and biomass data used for the random forest analyses, and the random forest model outputs and areas of extrapolation in GIS format. Please refer to the following citation for additional details on the data: Beazley, L., Kenchington, E., and Lirette, C. 2017. Species Distribution Modelling and Kernel Density Analysis of Benthic Ecologically and Biologically Significant Areas (EBSAs) and Other Benthic Fauna in the Maritimes Region. Can. Tech. Rep. Fish. Aquat. Sci. 3204: vi + 159p.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".