A geostatistical approach to cork production sampling estimation in <i>Quercus suber</i> forests
Bibliographic record
Abstract
The estimation of cork production in cork oak (Quercus suber L.) forests is complex because of the high heterogeneity of stripped surface distribution (the variable used to quantify cork production) and the importance of cork thickness estimation as a determining factor of cork quality. In this study, the different sources of variation in stripped surface ([Formula: see text]d) estimation and the effects of the spatial structure of the variance were analysed. When indicator kriging was used to determine the cork productive area, ordinary kriging and kriging with measurement errors gave better estimations of [Formula: see text]d (ordinary block kriging estimation of 156.16 m2/ha and standard errors (SE) of 16.40 and 15.7 m2/ha, respectively) than the design-based approach for the whole forest area (66.37 m2/ha, SE = 11.34 m2/ha). The SE lying in the second-stage design was 4.93 m2/ha. The ordinary kriging prediction of cork thickness using an XY(λZ) variogram, where λ is the anisotropy coefficient of the Z axis, gives a smaller SE and less bias than the kriging prediction with the XY variogram (for a mean estimation of 21.91 mm, SE = 3.90 and 4.16 mm, respectively, and sum of errors of 0.42 and 0.85 respectively).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".