Bibliographic record
Abstract
2. The chairman laid out broadly what the goals of the working group would be, emphasising that the distributed agenda was ambitious and that this meeting would be only the first of many required to formulate a plan for and develop/produce a State of the Marine Ecosystem Report. He then asked each participant to say a few words about their reasons for joining the working group. • A common theme in this discussion was the recognition of a need for the consolidation, synthesis and evaluation of the growing body of environmental and ecosystem data from Canadian east coast waters. • The Centre for Marine Biodiversity (CMB) was thought to be a logical organization to undertake the coordination of this activity and the production of the report(s). 3. Discussion then lead into why specifically CMB should undertake this task and who would be the target audience. • It was felt that the CMB would bring in more people with broader expertise and a broader vision for the report than if initiated by a government department, i.e. DFO or EC. • It was noted that a somewhat similar initiative was started in the early 90s (ECNASAP) with the view of consolidating, synthesising and interpreting trends in US (NOAA) and Canadian (DFO) groundfish data. This project, however, lacked the institutional framework for evaluating the results in the context of the state of the ecosystem. Such a framework now exists, Dunsmuir workshop on 2 objectives and indicators for ecosystem-based management, Report 2001/009
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.849 | 0.736 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".