Assessment tools are needed to support marine ecosystem-based management, but how to get them used practically?
Bibliographic record
Abstract
Ecosystem-based management (EBM) is essential to maintain healthy, productive and resilient marine ecosystems while sustainably providing ecosystem services leading to the goods and benefits humans want and need. Ecosystem status assessment is essential to the EBM process and there are many and varied methods (or tools) to undertake that assessment in support of EBM. This paper analyses these tools against the characteristics that make them most suited for practical implementation. A total of 34 tools were identified, including 18 generic and 16 specific tools. Information on the characteristics of the available tools was obtained via a structured online survey that was completed by 45 experts. The survey focused on: (i) the purpose and context of the use of a tool (e.g., the EBM elements it addresses, who uses it or in which context it is applied, and its relevance for marine governance); (ii) the type of assessment that the tool provides (e.g., which components of the accepted cause-consequence-response sequence are involved, what spatial and temporal scales are relevant to the assessment); (iii) the requirements of the tool in terms of data (type and variables), expertise/skills and other resources, and (iv) any strengths and weaknesses, including barriers for practical implementation. Similarities and differences in the expert responses were explored between the tools. Each tool was shown to have a specific combination of characteristics, which may make it more or less suitable for practical use depending on the EBM context and elements to which it is applied (i.e., one-size-fits-all does not apply). The tool suitability is also determined by the user-specific requirements for the assessment and this study provides a valuable means to inform the user and guide their decision on which tool(s) to use in the case-specific implementation of the EBM.
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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 | 0.072 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.044 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".