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Record W2617763292 · doi:10.1093/jee/tox149

Preservation of Domesticated Honey Bee (Hymenoptera: Apidae) Drone Semen

2017· article· en· W2617763292 on OpenAlexaff
Marilène Paillard, Andrée Rousseau, Pierre Giovenazzo, Janice L. Bailey

Bibliographic record

VenueJournal of Economic Entomology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité LavalCentre de Recherche en Sciences Animales de Deschambault
Fundersnot available
KeywordsBiologySpermCryopreservationCryoprotectantSemenInseminationAndrologyHoney beeArtificial inseminationZoologyBotanyAnatomyGenetics

Abstract

fetched live from OpenAlex

Preservation of honey bee (Apis mellifera L., Hymenoptera: Apidae) sperm, coupled with instrumental insemination, is an effective strategy to protect the species and their genetic diversity. Our overall objective is to develop a method of drone semen preservation; therefore, two experiments were conducted. Hypothesis 1 was that cryopreservation (-196 °C) of drone semen is more effective for long-term storage than at 16 °C. Our results show that after 1 yr of storage, frozen sperm viability was higher than at 16 °C, showing that cryopreservation is necessary to conserve semen. However, the cryoprotectant used for drone sperm freezing, dimethyl sulfoxide (DMSO), can harm the queen and reduce fertility after instrumental insemination. Hypothesis 2 was that centrifugation of cryopreserved semen to reduce DMSO prior to insemination optimize sperm quality. Our results indicate that centrifuging cryopreserved sperm to remove cryoprotectant does not affect queen survival, spermathecal sperm count, or sperm viability. Although these data do not indicate that centrifugation of frozen-thawed sperm improves queen health and fertility after instrumental insemination, we demonstrate that cryopreservation is achievable, and it is better for long-term sperm storage than above-freezing temperatures for duration of close to a year.

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 score0.672
Threshold uncertainty score0.235

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 head0.056
GPT teacher head0.261
Teacher spread0.205 · 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

Citations34
Published2017
Admission routes1
Has abstractyes

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