Relation between beluga whale aggregations and sea temperature on climate change forecasts
Bibliographic record
Abstract
Climate change has been shown to alter the spatial distribution of whales and other marine mammals. Fast changing ocean temperatures may also affect the spatial distribution of whales at a finer scale, namely within populations, including aggregation behaviour. Our ability to analyze the impact of climate change on whale aggregation behavior, however, has been limited by our ability to collect spatial observation data over large areas. To overcome this limitation, this study analyzed open-access satellite imagery obtained between 2007 and 2020 in Canada, Russia, and Alaska using Deep Convolutional Neural Networks (CNN) to detect 1,980 beluga whales in 11 populations and to quantify their aggregation patterns within their populations. Subsequently, we examined the relationship between sea surface temperature (SST) and the intra-population spatial patterns of beluga whales during summer seasons, when these whales normally aggregate. We detected a negative correlation between SST and the frequency of beluga whale aggregation, suggesting that warming temperatures may impact beluga whale spatio-behavioral dynamics. Considering that the relative abundance of beluga whales is declining and the future SST projections in these Arctic Ocean locations, climate change may pose yet another threat to beluga whales and other ice-dependent species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".