<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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".