1 Forest Landscape Baselines No. 14 Brief Progress and Summary Reports 1996 Status of Old-Growth Red Pine Forests: A Preliminary Assessment
Bibliographic record
Abstract
The purpose of this preliminary report is to address the current status of old-growth red pine forest given the information currently available. Information provided by forest experts from a total of 19 political jurisdictions in Canada and the United States that coincide with the natural range of red pine forest was used. In addition, it was estimated (from Burns and Honkala (1990)) that the size of the range of red pine is approximately 40 % less than the size of the range of eastern white pine. It was also estimated, using pollen data from Gordon (1990), that within its natural range, presettlement red pine abundance was only 50 % of that for presettlement eastern white pine. To estimate the amount of original old-growth red pine area, (1) the area of original old-growth eastern white pine (from Quinby (1993)) was reduced by 40 % from 6 million hectares to 3.6 million hectares to reflect the range size difference between red and eastern white pine, and (2) the 3.6 million hectare figure was reduced by 50 % to reflect the lesser abundance of red pine relative to eastern white pine resulting in an estimate of 1.8 million hectares of original old-growth red pine forest in eastern North America. Based on the survey of forest experts, a total of 10,417 hectares of old-growth red pine forest remains in eastern North America (see Tables 1 and 2). It is likey, however, that additional unidentified stands exist, primarily in Ontario. To account for these unidentified stands, the estimate of 10,417 hectares was
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".