Past and Future Grand Challenges in Marine Ecosystem Ecology
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it