A rules-based approach for predicting the eastern hemlock component of forests in the northeastern United States
Bibliographic record
Abstract
The expanding threat of hemlock woolly adelgid (Adelges tsugae Annand) infestation has generated interest in locating eastern hemlock ( Tsuga canadensis (L.) Carr.). Prior studies have incorporated remotely sensed imagery to detect eastern hemlock presence or absence. The goal of this study was to develop methodology to quantify hemlock abundance using software and data accessible to forest managers. Three seasons of Landsat ETM+ scenes served as the imagery basis, whereas simple (slope, aspect, and curvature) and detailed (heat and wetness) environmental indices were extracted from a digital elevation model. Three hundred and forty-nine forest plots representing the typical forest cover found in the Catskill Mountain Region, New York, served as ground reference; model input used the percentage of hemlock basal area for each plot. The models generally underpredicted in plots with substantial hemlock composition, whereas overpredictions mainly occurred in mixed forests that lacked hemlock. Underpredictions negated overpredictions in mixed hemlock deciduous forests resulting in a neutral model. Correlation coefficients ranged from a high of 0.67 for the model created from three Landsat images to a low of 0.01 for the heat and wetness indices model. Although the models were typically within 10% of field measurements, there was no overall benefit in including topographic indices for mapping hemlock abundance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".