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Record W1533231091 · doi:10.2741/3536

Neural mechanisms coordinating the female reproductive system in the locust

2009· article· en· W1533231091 on OpenAlexafffund
Babuci Angela

Bibliographic record

VenueFrontiers in bioscience · 2009
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsUniversity of TorontoHatch (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpermathecaHuman fertilizationReproductive systemBiologyCourtshipOvulationLocustReproductive behaviorMatingOffspringAnatomySpermZoologyEcologyMedicineEndocrinologyPregnancyBotanyPopulation

Abstract

fetched live from OpenAlex

The production of viable offspring is a complex task, involving courtship, mating, maturation of eggs, ovulation, fertilization of eggs, and oviposition. With particular regard to the female, the reproductive system must produce eggs at the appropriate time and deposit them after fertilization in an appropriate place. Thus, the various structures of the reproductive system must be tightly coordinated and integrated. This review focuses on the female reproductive system and the neural mechanisms that lead to its integrated control. Central pattern generators, that are linked, control oviposition digging behavior, and contractions of the lower lateral and upper common oviducts that lead to retention of eggs. Sensory neurons also provide information about the presence of an egg in the genital chamber via a feedback loop to coordinate the spermatheca and thereby, fertilization. Neuropeptides and amines can modulate central and peripheral control mechanisms. These neural mechanisms are integrated such as to produce coordinated behavior, leading to the accomplishment of the ultimate task, that of producing viable offspring.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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 designBench or experimental
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

Citations36
Published2009
Admission routes2
Has abstractyes

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