Reply to Comment on Grahl-Nielsen et al. (2003): sampling, data treatment and predictions in investigations on fatty acids in marine mammals
Bibliographic record
Abstract
Knowledge of predator-prey relationships for marinemammals is a prerequisite for understanding theecology of these top marine predators and for appro-priate management of marine resources. Sara Iverson’sresearch group at Dalhousie University and herresearch partners elsewhere have dedicated consider-able resources to attempt to develop a method fordetermining the diet of marine mammals via fatty acid(FA) signature analysis, FASA (see references in Thie-mann et al. 2004a, this volume). The most outspokenskepticism regarding the usefulness of FASA for thisspecific purpose has perhaps been put forward by ourgroup based at the University of Bergen and theNorwegian Polar Institute. With much at stake, it isunderstandable that Iverson and co-workers are sensi-tive to criticism implied by our data and our interpreta-tion of it. Discourse on our disparate views hasoccurred on several occasions including Smith et al.(1997), Thiemann et al. (2004b) and now in the Com-ment by Thiemann et al. (2004a) to our paper on polarbears (Grahl-Nielsen et al. 2003).The criticism of Grahl-Nielsen et al. (2003) by Thie-mann et al. (2004a) focuses on 3 main issues; they con-tend that (1) we use an inappropriate tissue samplingprotocol, (2) our statistical approach is invalid and (3) ourconclusions are unwarranted because we fail to dealwith expected metabolism of fatty acids in mammalianpredators. The contents of Thiemann et al. (2004a) areclosely tied to 2 publications from Iverson’s researchgroup, published after Grahl-Nielsen et al. (2003), Thie-mann et al. (2004b) and Iverson et al. (2004), so this replyaddresses these publications briefly, in addition to thesubject matter raised directly in Thiemann et al. (2004a).
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.044 | 0.074 |
| Insufficient payload (model declined to judge) | 0.009 | 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".