Report of the Study Group on Ecosystem Assessment and Monitoring (SGEAM)
Bibliographic record
Abstract
The presentations of national and international activities related to the work of SGEAM demonstrated that ecosystem-based management and the development of indicators have become adopted by certain Member Countries, notably; the UK, Norway, Canada and the Baltic States. The methods being developed by these countries will help influence the development and implementation of an ICES-wide ecosystem-based assessment and management approach. SGEAM therefore recommends that ICES establish Regional Ecosystem Groups, REGs, to undertake the compilation and assessment of the periodic status reports from the various ICES working groups. SGEAM recommends that ICES as a beginning establish an REG for the North Sea to meet the invitation from the Bergen Declaration, which was the conclusion of the Fifth North Sea Conference held in Bergen on 20 and 21 March 2002. SGEAM also recommends, as it did in 2001, that a REG for the Baltic Sea be established. SGEAM also proposes a framework for the preparation of environmental data and assessment reports on a regular basis. SGEAM also recommends its termination, but recommends that a permanent working group should be established. Although the participation at the SGEAM meetings has not been particularly representative for the combined activities of ICES, SGEAM is of the opinion that there is a need for a forum within ICES where questions on how the ecosystem-based management approach can be implemented can be raised and discussed. A permanent working group will also be a valuable forum for exchange of national views on the development of indicators and for harmonizing the various approaches to ecosystem 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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".