Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marine heatwaves (MHWs) are prolonged extreme oceanic warm water events. They can have devastating impacts on marine ecosystems — for example, causing mass coral bleaching and substantial declines in kelp forests and seagrass meadows — with implications for the provision of ecological goods and services. Effective adaptation and mitigation efforts by marine managers can benefit from improved MHW predictions, which at present are inadequate. In this Perspective, we explore MHW predictability on short-term, interannual to decadal, and centennial timescales, focusing on the physical processes that offer prediction. While there may be potential predictability of MHWs days to years in advance, accuracy will vary dramatically depending on the regions and drivers. Skilful MHW prediction has the potential to provide critical information and guidance for marine conservation, fisheries and aquaculture management. However, to develop effective prediction systems, better understanding is needed of the physical drivers, subsurface MHWs, and predictability limits. Prolonged extreme oceanic warm water events, known as marine heatwaves, can have devastating impacts on marine ecosystems. This Perspective explores the predictability of marine heatwaves, taking into account the physical drivers, monitoring and prediction approaches, and stakeholder considerations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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 it