Author Correction: Hotspots for rockfishes, structural corals, and large-bodied sponges along the central coast of Pacific Canada
Bibliographic record
Abstract
Author notes These authors contributed equally: Alejandro Frid, Madeleine McGreer and Kyle L. Wilson. Authors and Affiliations Central Coast Indigenous Resource Alliance, Campbell River, BC, Canada Alejandro Frid, Madeleine McGreer, Kyle L. Wilson & Tristan Blaine School of Environmental Studies, University of Victoria, Victoria, BC, Canada Alejandro Frid Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC, Canada Cherisse Du Preez Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, BC, Canada Tammy Norgard Authors Alejandro Frid View author publications You can also search for this author in PubMed Google Scholar Madeleine McGreer View author publications You can also search for this author in PubMed Google Scholar Kyle L. Wilson View author publications You can also search for this author in PubMed Google Scholar Cherisse Du Preez View author publications You can also search for this author in PubMed Google Scholar Tristan Blaine View author publications You can also search for this author in PubMed Google Scholar Tammy Norgard View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Alejandro Frid .
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.085 | 0.032 |
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".