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Record W1989778658 · doi:10.4003/0740-2783-23.1.183

The function of dart shooting in helicid snails*

2007· article· en· W1989778658 on OpenAlexafffund
Ronald Chase

Bibliographic record

VenueAmerican Malacological Bulletin · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMollusks and Parasites Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDartSpermathecaBiologySperm competitionSexual selectionSexual conflictZoologySpermComputer science

Abstract

fetched live from OpenAlex

Some stylommatophoran species, including several helicid snails common to Europe and North America, drive sharp, calcareous darts into their sexual partners prior to copulation. Why any animal would treat a prospective mate in this manner has been the subject of considerable speculation. One widely held belief is that the dart stimulates the partner. Here, I review evidence showing that this hypothesis, along with several others, is almost certainly incorrect. On the other hand, there is strong empirical support for the idea that the dart increases the reproductive fitness of the successful shooter by promoting the survival and utilization of its sperm. How the dart works to produce this effect is an open question; current evidence indicates that it injects a chemical agent into the recipient and that this substance contracts the female tract in such a manner as to facilitate the passage of allosperm to the spermatheca. Although successful dart shooting clearly benefits the shooter, there is little evidence to suggest either a cost or a benefit to the recipient.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2007
Admission routes2
Has abstractyes

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