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Record W1992006416 · doi:10.1093/conphys/cou044

Enzyme immunoassays as a method for quantifying hair reproductive hormones in two felid species

2014· article· en· W1992006416 on OpenAlexaffabout
C.V. Terwissen, Gabriela F. Mastromonaco, Dennis L. Murray

Bibliographic record

VenueConservation Physiology · 2014
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsToronto ZooTrent University
Fundersnot available
KeywordsCATSBiologyHormoneTestosterone (patch)EstrogenEndocrinologyInternal medicineEstrous cyclePhysiologyCaptivityZoologyMedicine

Abstract

fetched live from OpenAlex

Non-invasive monitoring of wild felid reproductive states is important, given that many species reproduce poorly in captivity. Despite extensive work in faecal hormone analysis in felids, continued development of techniques is necessary, particularly with wild populations. In this study, our aims were as follows: (i) biochemical validation of enzyme immunoassays for estrogen, testosterone and progesterone in Canada lynx and domestic cat hair extracts; (ii) assessment of the use of hair reproductive hormones to differentiate between reproductive states (intact, estrus, pregnant and spayed/neutered), using domestic cats as a model; and (iii) assessment of the use of hair reproductive hormones to differentiate between age and sex, accounting for potential regional variability in wild lynx populations. Analysis of hair hormone levels showed prospective value in detecting pregnancy states, with pregnant domestic cats having higher levels of progesterone than spayed females. However, intact and pregnant cats did not differ in progesterone levels. Yet, two female domestic cats had higher levels of hair progesterone following a 38-day oral progestin treatment, perhaps providing a preliminary pharmacological validation of the method. Estrogen and testosterone did not differ statistically according to reproductive states of domestic cats, although intact males had higher levels of hair testosterone than neutered males. When we applied these techniques to lynx fur, we determined that hormone levels were not sufficiently precise to differentiate age classes. Hair reproductive hormone ratios differed between sexes, with the estrogen-to-progesterone ratio demonstrating the highest accuracy in differentiating males from females. Hair hormone levels differed regionally for wild lynx, indicating that spatial variability should be a consideration in wildlife hormone studies spanning large spatial scales. We conclude that use of hair hormone analysis by enzyme immunoassay may hold promise for differentiating sex in felids, but the technique will require further refinement and validation before it can be applied broadly and reliably.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.950
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.142
GPT teacher head0.421
Teacher spread0.279 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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