Modelling attributes of rubberwood (Hevea brasiliensis) stands using spectral radiance recorded by Landsat Thematic Mapper in Malaysia
Bibliographic record
Abstract
Investigates the relationship between Landsat TM data and rubberwood stand parameters, and establishes and evaluates models for estimating stand volume and predicting area of rubber plantations in Malaysia. The total sample set of stands was divided into two independent groups: model-building and validation data sets. Regression analyses were used to explore relationships between volume and Landsat TM bands and ratio-based indices. Selected TM bands were found to be inversely related to stand volume (P<0.0001). Relations between TM data and measured stand volume were found to be significant (P<0.01), with an I/sup 2/ (correlation index square) of 0.79 and root mean squared error of 49.5 m/sup 3//ha. A logistic regression model produced classifications with an accuracy of 95% for predicting the area of rubber plantations. Thus, Landsat TM provides an acceptable data source for estimating wood volume and predicting area of rubber plantations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".