ESTIMATING TIMING OF LIFE-HISTORY EVENTS WITH COARSE DATA
Bibliographic record
Abstract
Populations often are sampled with a coarse scale of measurement. As scale becomes increasingly coarse, the variance estimate can become biased; Sheppard's method has been used to correct that bias. Sheppard's correction, however, also becomes inadequate when the scale of measurement is too coarse. We develop a rule to decide how coarse a scale should be for a particular population variance. In addition, we propose a generalization of Sheppard's correction that allows for divisions of the scale to be unequal. Divisions of a measurement scale (intervals or bins) should be no larger than twice the population SD (σ). When the population variance (σ2) is small, a large amount of variation in interval size can produce inaccurate results. As σ2 becomes large, more variation in interval size can be allowed without producing large inaccuracies. This new methodology has wide application for estimating timing of life-history events for mammals where dates must be pooled into intervals. We demonstrate this method with computer simulations and with an example for estimating mean and variance of birth date in Dall's sheep (Ovis dalli).
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".