Knowledge sharing about deep-sea ecosystems to inform conservation and research decisions
Bibliographic record
Abstract
The Marianas Trench Marine National Monument (MNM) currently extends policy-based protection to deep-sea ecosystems contained within it, but managers require better understanding of the current knowledge and knowledge gaps about these ecosystems to guide decision-making. To address this need, we present a case study of the Marianas Trench MNM using in-depth interviews to determine scientists’ (1) current understanding of anthropogenic drivers of change and system vulnerability in deep-sea ecosystems; and (2) perceptions of the least understood deep-sea ecosystems and processes in the Marianas Trench MNM, and which of these, if any, should be research priorities to fill knowledge gaps about these systems and the impacts from anthropogenic drivers of change. Interview respondents shared similar views on the current knowledge of deep-sea ecosystems and potential anthropogenic drivers of change in the Marianas Trench MNM. Respondents also identified trench and deep pelagic (bathyal, abyssal, and hadal zones) ecosystems as the least understood, and highlighted climate change, litter and waste, mining and fishing, and interactions between these drivers of change as critical knowledge gaps. To fill key knowledge gaps and inform conservation decision-making, respondents identified the need for monitoring networks and time-series data. Our approach demonstrates how in-depth interviews can be used to elicit knowledge to inform decision-making in data-limited situations.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".