Editorial: Solving Complex Ocean Challenges Through Interdisciplinary Research: Advances from Early Career Marine Scientists
Bibliographic record
Abstract
Advances from Early Career Marine ScientistsAnthropogenic impacts on the world's coasts and oceans are accelerating at an unprecedented rate, threatening marine biodiversity and ecosystem functioning, and with it the sustainability of the goods and services marine systems provide, with downstream impacts on societal well-being and livelihoods.Embedded within complex social-ecological systems, coasts and oceans are subject to uncertain, unpredictable, and interconnected challenges, to which solutions cannot be developed through single disciplinary approaches.To this end, there has been growing recognition of the need for interdisciplinary and transdisciplinary marine science to push towards sustainable, productive, and healthy coasts and oceans at a time of significant global change (Kelly et al., 2019).Early Career Researchers (ECRs) are at the forefront of this new research agenda, and this Research Topic showcases the diversity of research undertaken by the next generation of marine
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.021 | 0.023 |
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".