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Record W3093914189 · doi:10.1111/eth.13101

Males mate indiscriminately in the tropical jumping spider <i>Hasarius adansoni</i> (Audouin, 1826)

2020· article· en· W3093914189 on OpenAlexaff
Leonardo Castilho, Maydianne C. B. Andrade, Regina H. Macedo

Bibliographic record

VenueEthology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMatingOffspringSpermBiologySexual selectionMate choiceAntagonistic CoevolutionSpiderSperm qualityEcologyZoologyDemographySexual conflictPregnancy

Abstract

fetched live from OpenAlex

Abstract Traditionally, sexual selection has been seen as a process in which choosy females select non‐choosy males. Recent studies, however, have challenged this view by showing that males can also be choosy in many species. We assessed the sexual preferences in males of the tropical jumping spider Hasarius adansoni (Audouin, 1826). We measured mating effort in males and determined how female quality influences offspring quality, quantity, and survival. We also estimated total sperm load and how much sperm was invested in a mating with a particular female. We found no evidence of male mating effort in terms of mating frequency nor sperm investment. Similarly, there was no relationship between female quality (i.e., size and condition) and offspring quality (i.e., survival and feeding performance) or quantity. We found strong evidence that the sperm invested in a particular female is a function of the amount of sperm the male had available for usage at that particular mating. The fact that males probably find females sequentially, along with the lack of relationship between female quality and offspring quality/quantity, likely explains the lack of differences in mating effort by males.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.654
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

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

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 head0.080
GPT teacher head0.275
Teacher spread0.194 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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