Global Warming and Arctic Marine Mammals (GWAMM)
Bibliographic record
Abstract
The GWAMM project has developed a Community-Based Monitoring (CBM) network within the greater Hudson Bay region. Our goal was to monitor marine ecosystem change using apex predators but also to understand the drivers of change. Results provided policy information required to allow northerners the ability to adapt to the environmental changes. During the Inuit subsistence hunts of 2007, the network relied on Arviat and Sanikiluaq community involvement. In 2008 and 2009, we expanded the network to include the communities of Repulse Bay and Igloolik, respectively. Partnering with northerners provided whale and seal tissue samples from hunts as well as a collection of prey species representing parts of the marine environment. A reference collection of samples from the complete food web was developed and used to build a model of trophic interactions from marine mammals down to nutrients and phytoplankton. GWAMM is also a network project that links to other marine mammal research projects in the region including satellite-telemetry movement studies of polar bears, seals, and whales, and photo-identification of bowhead and killer whales, and use of chemical signals to understand whale and seal diet, and tracking predation effects caused by invasive species such as killer whales. Results indicated that with declining sea ice, the Hudson Bay marine ecosystem is shifting from a polar bear-seal system with Inuit hunters at the apex to one dominated by cetaceans with killer whales at the apex. This shift is eroding Inuit traditional subsistence culture. As a result, our project has the added goal t o provide northerners with information required to adapt to a rapidly changing world where Arctic marine mammal populations are showing demographic strain due to polar warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".