Reply to the comment by Dalby and Elliott on "Predator classification by the sea pen <i>Ptilosarcus gurneyi</i> (Cnidaria): role of waterborne chemical cues and physical contact with predatory sea stars"
Bibliographic record
Abstract
The comment of Dalby and Elliott was triggered by a statement we made in a research note to the effect that our study provided the first experimental evidence of predator-classification abilities in cnidarians. Based on an extensive literature search and the appreciation of our manuscript by the reviewers, at the time of publication we believed that this statement was correct. After reviewing the research articles highlighted by Dalby and Elliott, we concur that the statement is incorrect. We discuss some possible reasons why the articles cited by the authors were not found during our literature search, including the use of technical "jargon". In formulating their comment, the authors make several incorrect assumptions, including (i) that our literature search was limited to a single broad review paper and (ii) that we overlooked key information in at least two of the papers we cited. Also, the authors appear to confound predator recognition and predator classification, as some of the articles they cite do not examine predator-classification abilities. Finally, they give the impression that predator-classification abilities are ubiquitous in cnidarians. This appears to be an overstatement, since a number of published studies clearly indicate high variability in the ability of cnidarians to (i) recognize predators and (ii) respond "correctly" according to the relative threat they represent.
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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 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.051 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".