Bibliographic record
Abstract
Multiple fecundity (i.e., >2 fetuses or calves per female) is a rare and poorly known phenomenon in moose (Alces alces). In this paper I: (1) report the frequency of multiple fecundity of moose in Finland; (2) study the frequencies of multiple fecundity in different years and areas; (3) discuss the viability of litters with different numbers of progeny; and (4) discuss the possible fecundity effects of selective harvest and the evolutionary aspects of multiple fecundity. The embryo numbers of harvested cows were counted during 1980-89 (n = 2,347) and the proportion of single, twin, and triplet calves were determined from the 1986-99 moose observation material recorded in the field by hunters during the hunting season (n = 585,149). The material includes 4 sets of quadruplet calves, 1 set of stillborn sextuplets, and a moose female with 5 sets of triplet calves; a total of 30 calves in 15 years. In Finland, 60.38 % of pregnant moose cows had one, 39.37% two, 0.21% three, and 0.04% four embryos. In the observation material, 61.79% of the cows had one calf, 38.18% twin calves, and 0.03% triplet calves. The proportion of multiple cases decreased from south to north. The viability of single and twin calves was found to be very high, but only 15% of the sets of triplet calves seemed to survive up to the first fall. Calf survival rate was clearly higher in 1980-99 than in 1963-66, possibly depending on the different age structures of the female populations. According to the literature, the frequency of multiple fecundity in moose appears to be lower in North American than European moose populations. ALCES VOL. 39: 89-107 (2003)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".