Fisheries Centre research reports. Volume 11, number 2
Bibliographic record
Abstract
Director’s Foreword: Virtue on the Reef (Tony J. Pitcher). Introduction and Summary of Workshop (W. Seaman and B. Smiley). KEYNOTE PAPER. Data Rich and Conclusion Poor: How Can We Learn More for the Effort? (W. Lindberg). TECHNICAL REPORTS. Yukon Artificial Reef Monitoring Project (N. Barger). Project Emerald Sea: Volunteer Restoration and Monitoring of a Highly Disturbed Estuary(D. Biffard). The Living REEF Project: Monitoring Invertebrates in a Fish Monitoring Project (D. Haggarty and S. Francis). The Annual Lingcod Egg Mass Survey in British Columbia (J. Marliave). Developing a Marine Species List for Identification by Volunteers:Experiences of the Georgia Strait Alliance Inter-tidal Quadrat Studies (B. Nichols). Monitoring Bunny’s Web Reef off Jacksonville, Florida, and Challenges Facing a Volunteer Dive Team (J. Perkner, L. Waters and M. Dillon). Assessing the Habitat Productivity of Reefs Created from Blasted Rock (L.R. Russell). The REEF Fish Survey Project (C. Semmens). Reliability and Utility of Sidney Pier Artificial Reef Monitoring Data from Volunteer Reefkeeper Divers (B. Smiley and B. Burd). APPENDICES. 1. Workshop Agenda. 2. List of Participants. 3. Rapporteurs’ Record of Discussions. 4. Workshop Evaluation. 5. Presentations made at the workshop.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.522 | 0.391 |
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".