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Record W1534568889 · doi:10.1111/ede.12034

Mating system does not predict permanent sperm depletion in black widow spiders

2013· article· en· W1534568889 on OpenAlexafffund
Maria Modanu, Peter Michalik, Maydianne C. B. Andrade

Bibliographic record

VenueEvolution & Development · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCooperative Research Centres, Australian Government Department of IndustryDeutsche Forschungsgemeinschaft
KeywordsBiologySpermCannibalismMatingZoologyEcologyPolygynyMating systemFemale sperm storageSperm competitionSpiderPredationBotanyDemographyPopulation

Abstract

fetched live from OpenAlex

Variation in sperm production is strongly influenced by mating system across taxa. Recent work in spiders suggests that males of some species show termination of spermatogenesis before their adult molt and thus an inability to produce sperm after maturation. This permanent sperm depletion (PSD) has been hypothesized to co-occur with monogyny, genital mutilation, or sexual cannibalism because the maintenance of continual sperm supplies is not necessary for species where males can expect only one mating opportunity. Here we test this hypothesis in two congeners exhibiting genital mutilation: the sexually cannibalistic, monogynous Australian redback spider Latrodectus hasselti and the polygynous Western black widow Latrodectus hesperus. We report that PSD does not occur in adult males of either species, and show that males transfer sperm into their copulatory organs multiple times as adults. These data suggest evolutionary links between mating system and investment in sperm production may be more complex than currently appreciated.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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

Citations20
Published2013
Admission routes2
Has abstractyes

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