Bibliographic record
Abstract
Sector sampling is a new and simple approach to sampling objects or borders. This approach would be especially useful for sampling objects in small discrete areas or “polygons” with lots of internal or external edge, but it may be extended to sampling any object regardless of polygon size. Sector plots are wedge-shaped with a fixed sector angle. The probability of object selection is constant and equal to the sector angle in degrees divided by 360°. A unique property of sector sampling is that the point from which the angle originates may be located subjectively when the sector direction is at random. Another advantage over traditional sampling (such as fixed or variable area plots) is that there is no edge effect; that is, there is no altering of selection probabilities of objects close to polygon boundaries. Various approaches are described for deriving polygon means and totals with their associated variances. We review the genesis of sector sampling and develop two new components: sub-sampling using fixed area plots and line sampling using the sector arcs as transects. Sector sampling may be extended to measuring a variety of objects such as trees, shrubs, plants, birds, animal trails and polygon borders.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".