Eggs of spruce grouse dry at a faster rate than those of ruffed grouse
Bibliographic record
Abstract
We measured the rate of water loss and pore density of eggs of spruce grouse ( Canachites canadensis canace (L., 1766)) and ruffed grouse ( Bonasa umbellus togata (L., 1766)) from different parts of their range in Ontario. Eggs were dried in enclosed glass jars over Drierite®and in paper trays in open air at room temperature and humidity. Eggs were weighed to the nearest 0.01 g every 2–4 days and the change in mass was measured as water loss. Pores of shells were counted (pores/cm2) in the blunt, middle, and pointed sections of the egg. Eggs of spruce grouse lost water at a faster rate in Drierite®and in open air and had a greater density of pores than eggs of ruffed grouse. Rates of water loss were constant and varied inversely with ambient humidity, with the difference between species greatest in open air. Eggs late in incubation of ruffed grouse dried at a faster rate than those early in incubation in Drierite®. The adaptations of eggs of each grouse to the moisture of the nest may help explain their distribution, density, and habitat and nest-site selections, as well as behavioural aspects of the nesting hen. Both, especially the spruce grouse, may be good indictors of climate change.
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 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 | 0.001 | 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".