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Record W2044190139 · doi:10.1139/f00-097

Larval condition and vulnerability to predation: reply to comment by Suthers

2000· article· en· W2044190139 on OpenAlexvenueno aff
Joel K. Elliott, William C. Leggett

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsCapelinVariety (cybernetics)PredationLarvaBiologyVulnerability (computing)EcologyComputer scienceArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

Discussion 1538 Scientists recognize that observations and experiments at a variety of spatial and temporal scales and in a variety of different systems are required to obtain a complete understanding of a particular phenomenon. However, scientists also recognize that an individual study can usually only focus on one particular system at a certain time and place and that many different studies are required to achieve a general conclusion. The objective of our study on larval capelin (Mallotus villosus) (Elliott and Leggett 1998) was to provide an experimental test of whether larvae in poor condition had lower relative survival rates than larvae in good condition when exposed to a predator. We employed an experimental design that had been successfully used by a variety of other researchers (Litvak and Leggett 1992; Pepin et al. 1992; Pepin and Shears 1995; Elliott and Leggett 1996) to test selective mortality hypotheses under laboratory conditions in small aquaria. In our abstract, we explicitly stated that the results of our study “suggest that RNA/DNA ratios are not useful indicators of vulnerability to predation for larval capelin.” Clearly, we did not intend to have this conclusion be considered a broad statement that applied to all other systems. As in all scientific disciplines, we presented our data with the intention that other researchers would conduct further studies using a variety of experimental systems and protocols to provide further tests that would either support or refute our results. After a variety of studies on this topic had been conducted, we would hope that a metanalysis would provide a more complete understanding of the effects of larval condition on vulnerability to predation. Ultimately, the results of this research could lead to the incorporation of lar val condition indices into assessments of the future survival probabilities of larvae into fisheries models. Unfortunately, in the comment by Suthers, our conclusions have been overstated and interpreted as a definitive re jection of the utility of RNA/DNA ratios and larval dry weight in the study of larval condition. As stated above, our conclusions pertained to larval capelin and only in the context of the experimental conditions employed. Furthermore, in the second to last paragraph of Elliott and Leggett (1998), we explicitly stated that our conclusions are based on our results, some of which are in contrast with previous studies of larval capelin in small enclosures: “This difference in out come may result from the use of different experimental conditions and predator species or from the possibility that different measures of larval size (length versus dry weight) may not be equivalent in their influence on the vulnerability of larvae to predation.” In other words, variation in the spe cies used or experimental protocol could have caused a different result (as has been reported in many other studies of selective mortality). A general conclusion on the influence

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.003
Science and technology studies0.0040.009
Scholarly communication0.0040.013
Open science0.0100.005
Research integrity0.0520.081
Insufficient payload (model declined to judge)0.0080.008

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.

Opus teacher head0.017
GPT teacher head0.250
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2000
Admission routes1
Has abstractyes

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