A predictive model to diagnose pregnancy in guanacos (<i>Lama guanicoe</i>) using non-invasive methods
Bibliographic record
Abstract
Pregnancy status is usually not included in ecological studies because it is difficult to evaluate. The use of non-invasive methods to determine pregnancy, without physically restraining individuals, would enable pregnancy to be included in population studies. In this study, we evaluated sex steroid hormones in plasma and fecal samples from pregnant and non-pregnant females to develop a pregnancy predictive model for guanacos (Lama guanicoe (Müller, 1776)). Samples were obtained during live-shearing management (i.e., capture, shear, and release) of guanacos. Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) in feces. Mean hormonal and fecal metabolite concentrations were significantly higher in pregnant females than in non-pregnant females. A linear relationship was found between each hormone and its fecal metabolite. Finally, hormonal data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model to diagnose pregnancy in non-handled individuals. The use of predictive models and non-invasive methods might be suitable to incorporate pregnancy information in large-scale population studies on guanaco and other free-ranging ungulates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".