Theme Session L – Evaluating ecosystem-based management performance: examples of success (co-sponsored by PICES)
Bibliographic record
Abstract
Book of abstracts of theme session L:Evaluating ecosystem-based management performance: examples of successConveners: Alida Bundy (Canada), Janne Haugen (USA), Mark Dickey-Collas (UK/Netherlands), Xuelei Zhang (China)CM 694: Design and implementation of compensation framework for marine ecological loss based on ecosystem services: a case study in China Shang Chen, Shuai He, Tao Xia, Linhua HaoCM 704: Status and prospect of ecosystem-based fisheries management in ChinaCM 753: Handing over the ecosystem approach to fisheries (EAF) baton to the fishing industry – a possible way for South Africa to progress ecosystem-based management within the fishing sectorCM 782: Developing EBM performance measures when some objectives are more equal than othersCM 793: Quantifying ecosystem-based management efficacy: A multifaceted performance assessmentCM 868: The elephant in the room: ecosystem-based management in CanadaCM 873: Local Ecological Knowledge and Ecosystem-Based Management: Insights from the CaribbeanCM 1100: A framework and tool for assessing ecosystem-based marine spatial planningCM 1118: Test, learn, adapt: applying policy evaluation to understand the impact of ecosystem based managementCM 1137: The ICES Framework for Ecosystem-Informed Science and Advice (FEISA): risk-based integration and performance evaluation across natural and social sciencesCM 1191: Past insights for modern ecosystem-based management: what can we learn from past practices?CM 1192: Originating from Values: Ecosystem-based Management in the Vega Archipelago, NorwayCM 1218: Concept usage and ambiguity in the (Dutch) North Sea governance system for applying ecosystem-based management
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.158 | 0.065 |
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".