Conifers of the New England–Acadian Forest: A Cultural History
Bibliographic record
Abstract
"Why did white pine help spark the American Revolution? How did balsam aid the development of germ theory? What does hemlock have to do with making leather? In Conifers of the New England-Acadian Forest, microbiologist Steve Keating explores how conifers influenced the course of human history, writing in a style that is both scientific and accessible. Keating's study focuses on one of the most forested and wild ecoregions in North America, which extends into New York, New England, and Canada and includes Acadia National Park. Here, spruces, firs, and cedars of the northern boreal forest mix with hemlocks and pines of more temperate climates. This combination helps create the appearance, aroma, and ecology of the region, and the trees' unique botanical traits have been ingeniously utilized by numerous peoples including Iroquois and French explorers, beer brewers, and shipbuilders. Keating concludes with identification guides for the conifers and where they can be found in Acadia National Park"--
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".