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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".