Quantifying marine mammal hotspots in British Columbia, Canada
Bibliographic record
Abstract
Abstract Global biodiversity is undergoing rapid decline due to direct and indirect anthropogenic impacts to species and ecosystems. Marine species, in particular, are experiencing accelerated population declines leading to many species being considered at risk by regional, national, and international standards. As one conservation approach, decisions made using spatially explicit information on marine wildlife populations have the potential to facilitate recovery and contribute to national and international commitments toward conservation targets. Delineating areas of intense use by species at risk can inform future marine spatial planning and conservation efforts, including the identification of marine protected areas. Methods for detecting hotspots (e.g., areas with high density and/or abundance) enable categorical mapping of the most intensely used areas. Yet, many of the current methods for delineating hotspots, such as the top 5% threshold, are subjective and fail to account for spatial patterns. Our goal was to map spatially continuous distributions of marine mammal densities and employ quantitative statistical methods to extract hotspot locations on the northern coast of British Columbia. We integrated systematically surveyed species information with environmental variables using generalized additive models to predict marine mammal distribution and density. Hotspots were identified from the density surfaces using two approaches: aspatial top 5% method and spatially local G i * statistic using three neighborhood definitions. Heterogeneous density patterns were observed for all species, and high‐density regions were generally clustered in areas exhibiting oceanographic characteristics that may promote concentrated food resources. Combining species density surfaces and extracting hotspot locations identified regions important to multiple species and present candidate locations for future conservation efforts. Contributions from this research provide robust statistical methods to objectively map hotspot locations and generate GIS data products for informing coastal conservation decisions.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".