Characterization of marine heat waves in the Iberia–Biscay–Ireland (IBI) region in 2022
Bibliographic record
Abstract
Abstract. Marine heat waves (MHWs) are defined as prolonged periods of anomalously high sea surface temperatures. These events have a profound impact on marine ecosystems, resulting in ecological and economic impacts such as coral bleaching, reduced surface chlorophyll due to increased surface layer stratification, mass mortality of marine invertebrates due to heat stress, rapid species migrations, and fishery closures or quota changes, among others. This research focuses on the study of the MHWs that occurred in the IBI (Iberia–Biscay–Ireland) region during the year 2022, assessing their climatologic properties, analyzing the mean values for the year 2022, and discretizing the events in four subregions representative of the entire domain. Satellite-derived sea surface temperature data were used to detect and characterize the events, revealing that in some areas the year 2022 showed peak anomaly values of (i) 15 MHW events, (ii) 128 d of mean durations, and (iii) 261 total days of MHWs. Through observational and modeling data, the discrete events located in the Bay of Biscay were also examined in the subsurface layers, demonstrating a strong seasonal modulation and heat diffusion through deeper layers, where cold-season events reach higher MHW mean depth values and subsurface positive anomalies of temperature can remain for weeks once an MHW has ended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".