Methods for Mapping Forest Sensitivity to Acid Deposition for Northeastern North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT For comparison purposes, two methods are proposed for mapping sustainable acid deposition within the context of natural and managed (harvested) forest biomass growth in Northeastern North America. One method uses existing geospatial data for forest cover type, soil type, local climate, topography, and atmospheric deposition. The other method uses data specific to well‐studied sites. Maps will be developed that show the spatial distributions of sustainable acid deposition rates by tree type, eco‐unit, and local forest disturbance regimes (by harvest method). Additional maps will be produced to show where these rates are likely exceeded, and by how much. The information so generated will be presented to policy and decision makers who deal with forest health and abatement control measures regarding regional sulfur (S) and nitrogen (N) emissions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 it