From iconic to overlooked species: How (electronic) tags improve our understanding of marine ecosystems and their inhabitants
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Matthias Schaber (Germany), Derke Snodgrass (USA).CM 2017/D:471. Who ate my science project...twice?. Matthias Schaber, Heino Fock, Péricles Neves SilvaCM 2017/D:354. If you can’t beat them, eat them: using acoustic telemetry to develop an economically viable fishery for the highly invasive roundgoby (Neogobius melanostomus). Mads Christoffersen, Jon C. Svendsen Jane Behrens Niels Jepsen Mikael van DeursCM 2017/D:322. Coupling spectral analysis and Hidden Markov Models for the segmentation of behavioural patterns: European sea bass as astudy case. Mathieu Woillez, Karine Heerah, Ronan Fablet, François Garren, Stéphane Martin, Hélène De PontualCM 2017/D:323. Comparison of geolocation models for the analysis of the spatial dynamics and population structure of pelagic fish: application toEuropean sea bass (Dicentrarchus labrax). Serena Wright, Mathieu Woillez, Victoria Bendall, Karine Heerah, David Righton, Ewan Hunter, Hélène de PontuaCM 2017/D:155. Movement and diet: Individual specialisations of barbel Barbus barbus in a disconnected and subsidised world. Catherine Gutmann Roberts, Andrew Hindes Robert BrittonCM 2017/D:477. Fine and broad-scale movement ecology of yellowtail snapper Ocyurus chrysurus within a marine protected area in the U.S. Virgin Islands. Ashleigh Novak, Sarah Becker, Jack Finn, Clayton Pollock, Zandy Hillis-Starr, Adrian JordaanCM 2017/D:315. Using acoustic telemetry to monitor competitive interactions between European Seabass (Dicentrarchus labrax) and giltheadseabream (Sparus aurata) in Salcombe Harbour, UK. Jenifer Lewis, Thomas Stamp, Ewan Hunter, Tim Robbins, Libby Ross, Frank van Veen, Emma SheehanCM 2017/D:286. Project I-BASS (preliminary results): Using acoustic telemetry to measure the effectiveness of spatial management for EuropeanSeabass (Dicentrarchus labrax). Thomas Stamp, Shaun Plenty, Tim Robbins, Libby Ross, Emma SheehanCM 2017/D:526. Lobster and crab movement around salmon farms in New Brunswick, Canada. Christopher W. McKindsey, Rénald Belley, Annick Drouin, Shawn M. C. Robinson, Émilie SimardCM 2017/D:517. IGFA Great Marlin Race: Largest citizen science billfish satellite tagging project. Leah Baumwell, Jason SchratwieserCM 2017/D:596. Northern Fur Seals and Pollock in the Eastern Bering Sea. Ivonne Ortiz, Jeremy SterlingCM 2017/D:339. A GPU-accelerated particle filter geolocation method for demersal fish using archival tagging data. Chang Liu, Chang Liu, Geoffrey CowlesCM 2017/D:381. Acoustic tags reveal the influence of electromagnetic fields on migratory and electro-sensitive species in an in situ controlled mesocosm study. Zoe L Hutchison, Andrew B. Gill, Peter Sigray, John King
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".