The effect of requeening in late July on honey bee colony development on the Northern Great Plains of North America after removal from an indoor winter storage facility
Bibliographic record
Abstract
SummaryThe effect of late summer requeening on the subsequent development of honey bee colonies during autumn (Harris, 2008b), and when confined in an indoor wintering facility (Harris, 2009) was extended with observations on sealed brood production, colony size and colony demographics every twelve days from 11 March until 14 August after they were removed from their winter quarters. Average adult populations declined for the first 48 days, and then recovered over the next 24 to 36 days once adult emergence consistently exceeded worker mortality. Rates of mortality for wintered workers were similar to those recorded for bees emerging in April, May, June, July and most of August. The last surviving bees from worker cohorts marked in September and October 1976 died between 3 June and 15 June 1977. Requeening treatment effects were quite variable and not statistically different. Requeened colonies were, however, generally larger than those headed by older queens when the experiment was terminated on 14 August and these colonies were killed and counted. The nine largest colonies belonged to the requeened treatments and contained on average 8,637 more bees (range 85 to 17,735) than the largest colony that had not been requeened. One of the requeened colonies was estimated to have contained slightly more than 80,000 adult bees at its peak population on 9 July.
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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 | 0.000 | 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".