Editorial: Impact of extreme climate events on marine ecosystems: Adaptation and challenges
Bibliographic record
Abstract
Impact of extreme climate events on marine ecosystems: Adaptation and challengesMarine ecosystems provide diverse and essential services to humankind, but multiple stressors (e.g., overfishing, climate change, and pollution) are reshaping ecosystem structures with potentially negative impacts on ecosystem functions (Doney et al., 2012;Costanza et al., 2014;Bland et al., 2018).Especially, extreme climate events (ECEs) are found to have profound and diverse impacts on marine populations and ecosystems (Wernberg et al., 2013;Zhang, 2020).In recent decades, the frequency and intensity of ECEs, e.g., tropical cyclones, and heatwaves, tend to increase in marine ecosystems (Kendrick et al.).In contrast to gradual climate change, ECEs feature drastic and rapid changes in environmental conditions.These sudden changes can affect both the structure and function of marine ecosystems in a way that resembles pulse disturbance (Smale et al., 2019;Zhang et al., 2022).Consequently, novel responses to ECEs may be observed in the variations in the physiological functioning, behavior, and demographic traits of marine organisms, shifts in the size structure, spatial range, and seasonal abundance of populations, and changes in the community structure and ecosystem function.To cope with challenges associated with ECEs, it is important to understand the mechanisms via which they affect marine ecosystems.Therefore, the purpose of this special issue is to understand the effects of ECEs on marine ecosystems at different levels and seek management solutions.The main results of papers submitted to this special issue are:Frontiers in Marine Science frontiersin.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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".