A cautionary note on the minimum crown cover criterion in forest definitions
Bibliographic record
Abstract
Forest area and its changes are understood as an important and, supposedly, easily measurable indicator for sustainable management of natural resources in larger areas. The observation and estimation of forest area must be based upon a clear definition. The minimum crown cover percentage is, in many forest definitions, a central element. This paper illustrates that any definition of a minimum cover percentage must be complemented by a definition of the sampling unit, which is used as a reference area on which the percent cover is to be determined. Otherwise, the results are not unique. A simple theoretical example and an aerial photograph are analyzed to illustrate these relations. The examples underline that, for the same minimum crown cover, the forest cover estimates vary considerably when the size of the sampling unit is changed. In general, for small values of the minimum crown cover as they are commonly used in forest definitions (0.1 to 0.3, say), the expected value of the cover estimate increases consistently with increasing size of the sampling unit on which the cover measurement is done. This effect is the more pronounced the more fragmented the forest cover and the more open the forest formations are.
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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 | 0.074 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.008 | 0.037 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".