The pre-European settlement forest composition of the Miramichi River watershed, New Brunswick, as reconstructed using witness trees from original land surveys
Bibliographic record
Abstract
The goal of this investigation was to characterize the pre-European settlement forest composition of the Miramichi River watershed using witness trees to contribute to the definition of a baseline for assessing changes over time in the Acadian forest. The witness tree data were stratified by ecoregion and by ecosite, for the portions of the watershed that are in the Northern Uplands, Continental Lowlands, and Eastern Lowlands ecoregions of New Brunswick, as well as by riparian and inland forest; and pre-settlement forest composition (1787–1847) was compared with current forest composition (1998–2000). The witness tree data constitute evidence that a distinctive riparian forest existed before European settlement and that the difference between riparian and inland forest has lessened. They show that the proportions of Betula spp., Tsuga canadensis (L.) Carrière, Ulmus americana L., and Thuja occidentalis L. have decreased; that the proportion of Acer spp. has increased and that Picea spp. and Abies balsamea (L.) Mill. var. balsamea have maintained their overall dominance of the forest in number of individuals, and have increased it on at least 50% of the watershed area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".