Bibliographic record
Abstract
The substrate demands of lactation must be met by increased dietary intake or by mobilization of nutrients from tissues. The capacity of animals to rely on stored nutrients depends to a large extent on body size; large animals have greater stores, relative to the demands of lactation, than do small animals. The substrate demands of lactation depend on the composition and amount of milk produced. Animals that fast or feed little during lactation are expected to produce milks low in sugar but high in fat, in order to minimize needs for gluconeogenesis while sustaining energy transfers to the young. The patterns of nutrient transfer are reviewed for four taxonomic groups that fast during part of or throughout lactation: sea lions and fur seals (Carnivora: Otariidae), bears (Carnivora: Ursidae), true seals (Carnivora: Phocidae) and baleen whales (Cetacea: Mysticeti). All these groups produce low-sugar high-fat milks, although the length of lactation, rate of milk production and growth of the young are variable. Milk protein concentrations also tend to be low, if considered in relation to milk energy content. Maternal reserves are heavily exploited for milk production in these taxa. The amounts of lipid transferred to the young represent about one-fifth to one-third of maternal lipid stores; the relative amount of the gross energy of the body transferred in the milk is similar. Some seals and bears also transfer up to 16-18 % of the maternal body protein via milk. Reliance on maternal reserves has allowed some large mammals to give birth and lactate at sites and times far removed from food resources.
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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) | 0.001 | 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".