“Distance-variable” estimators for sampling and change measurement
Bibliographic record
Abstract
The estimation procedure described is a simple technique that is applicable to virtually any plot-based sampling method and virtually any measured variable. It can be retrofitted to any existing fixed or variable plot over time by simply knowing the distance from the sampled object to the sample point. These estimators are illustrated for sampling over time as the plot size changes. An example is variable-plot sampling in forestry. Traditional estimates from sample plots can be geometrically viewed as a series of “disc shapes” where the same estimate is used for an object no matter how near the sample point is to that selected object. “Distance-variable” (DV) or “shaped” estimators have the same average value over the plot area, with some very important advantages. We believe that the DV estimate will be shown to reduce the variance of growth measurement compared with simple difference estimators. Traditional “disc” estimators are a special case of the more general DV estimators. There are no difficulties with the use of current edge-effect correction techniques, and the calculation of statistics is virtually identical to traditional methods.
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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 | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".