Effects of provisioning on shark behaviour: Reply to Brunnschweiler & McKenzie (2010)
Bibliographic record
Abstract
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 420:285-288 (2010) - DOI: https://doi.org/10.3354/meps08940 REPLY COMMENT Effects of provisioning on shark behaviour: Reply to Brunnschweiler & McKenzie (2010) Eric Clua1,*, Nicolas Buray2,3, Pierre Legendre4, Johann Mourier2,3, Serge Planes2,3 1Secretariat of the Pacific Community, BPD5, Noumea, New Caledonia 2Ecole Pratique des Hautes Etudes, UMR 5244 CNRS-EPHE-UPVD, Laboratoire "Ecosystèmes Aquatiques Tropicaux et Méditerranéens", Université de Perpignan, 66860 Perpignan, France 3Centre de Recherches Insulaires et Observatoire de l'Environnement (CRIOBE – UMS 2978 EPHE CNRS), BP 1013, 98729 Moorea, French Polynesia 4Département de sciences biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec H3C 3J7, Canada *Email: ericc@spc.int ABSTRACT: Brunnschweiler & McKenzie (2010; Mar Ecol Prog Ser 420:283–284) expressed reservations over the findings of Clua et al. (2010; Mar Ecol Prog Ser 414:257–266), mostly related to the lack of a reference site or a control group in the methodology. In our study, we distinguished between 39 individuals of sicklefin lemon sharks Negaprion acutidens, mainly based on photo-identification. Our study was based on the field-survey approach, with time (a continuous variable) as the source of variation, and thus a control group was not necessary. We provide here additional data that support the notion that abundance of lemon sharks on the provisioning site was increasing, both in their number and fidelity. We maintain our conclusion that sicklefin lemon shark provisioning off Moorea Island can continue, but should be more intensely controlled. KEY WORDS: Field survey approach · Lack of control site · Shark abundance · Site fidelity · Shark conservation Full text in pdf format PreviousCite this article as: Clua E, Buray N, Legendre P, Mourier J, Planes S (2010) Effects of provisioning on shark behaviour: Reply to Brunnschweiler & McKenzie (2010). Mar Ecol Prog Ser 420:285-288. https://doi.org/10.3354/meps08940 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 420. Online publication date: December 16, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".