Pollutants Affecting Endangered Whales and their Prey
Bibliographic record
Abstract
Pollutants Affecting Endangered Whales and their Prey: The science behind a new web application for environmental monitoring data Canada’s Recovery Strategy (RS) and Action Plan under the Species at Risk Act for the Northern and Southern Resident Killer Whales (Orcinus Orca) lists environmental contaminants as a key threat to viability and recovery, and recommends identifying and prioritizing key contaminants, and their sources. The RS also identifies the need to close certain data gaps, such as all potential anthropogenic environmental contaminants to which killer whales and their prey are exposed over time and in space. Not all sources of pollution within the spatial extent (the area where pollution could affect SRKW, including Resident Killer Whale critical habitat and Chinook salmon distribution in the Fraser Basin and coastal areas) were known, and many locations did not have associated wastewater or ambient monitoring data. To address this knowledge gap, we collated and analyzed available environmental monitoring, literature, and geospatial data. For locations were data were not available, we used modelling and statistical approaches (extrapolation, interpolation, and correlations) to fill in data gaps, creating a comprehensive geospatial database of characteristic contributions per sector/activity and ambient contaminant loads. Visualized using an online mapping tool, this inventory addresses several recommendations for SRKW Recovery by identifying sources of priority pollutants, contaminant hot-spots, locations of exceedances of environmental quality guidelines, and enabling a comparison of releases to loads which assists in identifying where data gaps and uncharacterized sources remain. The certainty of the estimates relies on availability of source-specific monitoring data, and some major contributors of contaminants to the spatial extent, such as stormwater runoff, have very little associated data. As more data becomes available, work will continue to improve the estimates and update the inventory, and include other endangered whales.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".