Past and Future Grand Challenges in Marine Ecosystem Ecology
Bibliographic record
Abstract
Frontiers in Marine Science launched the Marine Ecosystems Ecology (FMARS-MEE) section in 2014, with a paper that identified eight grand challenges for the discipline (Borja, 2014). Since then, this section has published a total of 370 papers, including 336 addressing aspects of those challenges. As editors of the journal, with a wide range of marine ecology expertise, we felt it was timely to evaluate research advances related to those challenges; and to update the scope of the section to reflect the grand challenges we envision for the next 10 years. This output will match with the United Nations (UN) Decade on Oceans Science for Sustainable Development (DOSSD;Claudet et al., 2020), UN Decade of Ecosystems Restoration (DER; Young and Schwartz, 2019), and the UN Sustainable Development Goals (SDGs; Visbeck et al., 2014). First, we analyzed each published paper and assigned their topic to a maximum of two out of the eight challenges (all information available in Supplementary Table 1). We then extracted the 3–5 most cited papers within each challenge using two criteria: the total number of citations during this 6-year period, and the annual citation rate (i.e., the mean annual number of citations since publication). We then collated the topics covered by this reduced list of papers (Table 1) and summarized the outcomes for each topic.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".