Marine mammal hotspots in the Greenland and Barents Seas
Bibliographic record
Abstract
Environmental change and increasing levels of human activity are threats to marine mammals in the Arctic. Identifying marine mammal hotspots and areas of high species richness are essential to help guide management and conservation efforts. Herein, space use based on biotelemetric tracking devices deployed on 13 species (ringed sealPusa hispida, bearded sealErignathus barbatus, harbour sealPhoca vitulina, walrusOdobenus rosmarus, harp sealPagophilus groenlandicus, hooded sealCystophora cristata, polar bearUrsus maritimus, bowhead whaleBalaena mysticetus, narwhalMonodon monoceros, white whaleDelphinapterus leucas, blue whaleBalaenoptera musculus, fin whaleBalaenoptera physalusand humpback whaleMegaptera novaeangliae; total = 585 individuals) in the Greenland and northern Barents Seas between 2005 and 2018 is reported. Getis-OrdGi* hotspots were calculated for each species as well as all species combined, and areas of high species richness were identified for summer/autumn (Jun-Dec), winter/spring (Jan-May) and the entire year. The marginal ice zone (MIZ) of the Greenland Sea and northern Barents Sea, the waters surrounding the Svalbard Archipelago and a few Northeast Greenland coastal sites were identified as key marine mammal hotspots and areas of high species richness in this region. Individual hotspots identified areas important for most of the tagged animals, such as common resting, nursing, moulting and foraging areas. Location hotspots identified areas heavily used by segments of the tagged populations, including denning areas for polar bears and foraging areas. The hotspots identified herein are also important habitats for seabirds and fishes, and thus conservation and management measures targeting these regions would benefit multiple groups of Arctic animals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".