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Record W2324356413 · doi:10.1139/cjz-2014-0132

Evidence for multiple paternity in two species of <i>Orconectes</i> crayfish

2014· article· en· W2324356413 on OpenAlexvenueno aff
Ariel F. Kahrl, Roger H. Laushman, Angela J. Roles

Bibliographic record

VenueCanadian Journal of Zoology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCrayfishBiologyZoologyEcologyMatingBroodContext (archaeology)DecapodaCrustacean

Abstract

fetched live from OpenAlex

Multiple mating is expected to be common in organisms that produce large clutches as a mechanism by which sexual reproduction can enrich genetic variation. For freshwater crayfish, observation of multiple mating suggests the potential for high rates of multiple paternity, but genetic confirmation is largely lacking from natural populations. We studied paternity within wild-caught broods of two crayfish species in the genus Orconectes (Sanborn’s crayfish (Orconectes sanbornii (Faxon, 1884)) and the Allegheny crayfish (Orconectes obscurus (Hagen, 1870))). Although females have been observed mating with multiple males, this is the first genetic confirmation of multiple paternity in broods of these two species. Berried females were collected in the field and maintained in aquaria until their eggs hatched. We amplified and genotyped extracted DNA from maternal and hatchling tissue for several microsatellite loci. For both species, paternity reconstruction (GERUD 2.0) yielded 2–3 sires per brood and no single paternity clutches. We discuss these results from natural populations in light of the body of work on reproductive ecology of decapod crustaceans and in the context of changes in life history following the transition from marine to freshwater habitats.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.250
Teacher spread0.213 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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Same venueCanadian Journal of ZoologySame topicCrustacean biology and ecologyFrench-language works237,207