A comparison of pre-European settlement forest characterization methodologies
Bibliographic record
Abstract
The characterization of "natural" or "presettlement" forest has become a relatively common practice in Canada as forest managers strive to put into practice concepts of sustainable forest management. Various methods have been developed to undertake such characterizations, leading to confusion about how to define "presettlement forest" and uncertainty over the approach that will best serve as a basis for management. We report on two methods of presettlement forest characterization: the "Witness Tree" and the "Potential Forests" approaches. We compare results from these approaches to the existing forest composition in the Fundy Model Forest, New Brunswick. Both approaches indicate a decline in the predominance of tolerant hardwood and eastern cedar since presettlement. However, the Potential Forests approach consistently suggests much higher presettlement frequencies of spruce (Picea spp.) and, in most cases, pine (Pinus spp.) than the Witness Tree method. Differences between frequencies of tree species estimated by the two methods probably result from biases associated with both methods and the different scales of reporting. If used critically, the combined use of both sets of presettlement forest information will allow managers to determine the historical frequency of individual tree species and forest communities. Such information will provide some guidance in maintaining the diversity of native species and community types. Key words: presettlement forest, ecological land classification, Witness Tree method, Potential Forests, forest management, biodiversity
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".