Seasonal changes in testicular size and serum LH, prolactin and testosterone concentrations in male polar bears (Ursus maritimus)
Bibliographic record
Abstract
Little is known about the reproductive endocrinology of the male polar bear, Ursus maritimus, except that serum testosterone concentrations are high in April and May during the mating season and are low from August to November during the non-mating season. The objective of this study was to describe the relationship between seasonal changes in testicular size and serum concentrations of testosterone, LH and prolactin. Blood samples and testicular measurements were obtained from free-ranging male polar bears in Canada in April (n = 5) and May (n = 15) near Resolute Bay, Northwest Territories and near Churchill, Manitoba in July (n = 15) and October (n = 22). Testis size was greater in May (39.4 +/- 3.5 cm(2)) than in October (27.3 +/- 2.0 cm(2)) (P = 0.002). Serum testosterone concentrations were approximately three-fold higher in April (5.8 +/- 0.8 ng ml(-1)) than in May (1.7 +/- 0.5 ng ml(-1)), July (0.6 +/- 0.2 ng ml(-1)) and October (1.1 +/- 0.2 ng ml(-1)). Similarly, serum LH concentrations were high in April (0.14 +/- 0.04 ng ml(-1)) and low in May (0.09 +/- 0.01 ng ml(-1)), July (0.10 +/- 0.02 ng ml(-1)) and October (0.08 +/- 0.00 ng ml(-1)). Serum prolactin concentrations were high in April (1.9 +/- 0.3 ng ml(-1)), highest in May (2.5 +/- 0.2 ng ml(-1)), lower in July (1.3 +/- 0.1 ng ml(-1)) and lowest in October (0.8 +/- 0.07 ng ml(-1)). The present study demonstrates a positive relationship between testicular size and serum concentrations of LH, prolactin and testosterone in the male polar bear and confirms the previously reported seasonal changes in serum testosterone concentrations. Data from the present study provide important baseline and comparative endocrine information that can be used to aid captive breeding programmes in zoos and to further ecological-behavioural studies of polar bears.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".