Anthropogenic and Natural Influences on Record 2016 Marine Heat waves
Bibliographic record
Abstract
In 2016 a quarter of the ocean surface experienced either the longest or most intense marine heatwave (Hobday et al. 2016) since satellite records began in 1982. Here we investigate two regions Northern Australia (NA) and the Bering Sea/Gulf of Alaska (BSGA) which, in 2016, experienced their most intense marine heat waves (MHWs) in the 35-year record. The NA event triggered mass bleaching of corals in the Great Barrier Reef (Hughes et al. 2017) while the BSGA event likely fed back on the atmosphere leading to modified rainfall and temperature patterns over North America, and it is feared it may lead to widespread species range shifts as was observed during the Blob marine heat wave which occurred immediately to the south over 201315 (Belles 2016; Cavole et al. 2016). Moreover, from a climate perspective it is interesting to take examples from climate zones with very different oceanographic characteristics (high-latitude and tropics). We demonstrate that these events were several times more likely due to human influences on the climate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".