<i>Sonic Sea</i> Reveals Whales Are at Risk in an Ocean of Noise … But What about Other Sea Life?
Bibliographic record
Abstract
FisheriesVolume 42, Issue 5 p. 291-291 Movie Review Sonic Sea Reveals Whales Are at Risk in an Ocean of Noise … But What about Other Sea Life? Sarika Cullis-Suzuki, Sarika Cullis-Suzuki sarikacullissuzuki@gmail.com Ocean Networks Canada, Technology Enterprise Facility, Room 155, 2300 McKenzie, Gabriola Rd., Victoria, BC, V8P 5C2 CanadaSearch for more papers by this author Sarika Cullis-Suzuki, Sarika Cullis-Suzuki sarikacullissuzuki@gmail.com Ocean Networks Canada, Technology Enterprise Facility, Room 155, 2300 McKenzie, Gabriola Rd., Victoria, BC, V8P 5C2 CanadaSearch for more papers by this author First published: 05 May 2017 https://doi.org/10.1080/03632415.2017.1305856Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume42, Issue5May 2017Pages 291-291 RelatedInformation
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.087 |
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".