Reflections on the contributions of <i>Populus</i> research at Rhinelander, Wisconsin, USA
Bibliographic record
Abstract
The United States Department of Agriculture Forest Service established three regional Institutes of Forest Genetics in the United States in the 1950s to improve trees for reforestation and improve the management of forests. The institute in Rhinelander, Wisconsin, started in 1956 as part of the Lake States Forest Experiment Station. Since that time, the “Rhinelander Lab” has undergone changes in research priorities, organizational changes, and name changes while becoming an international center of forest scientific excellence. Many of the researchers’ key findings over the years were published in the Canadian Journal of Forest Research. In this paper, for the 50th anniversary edition of the Journal, we reflect upon one part of those accomplishments: the history of the contributions of Populus L. research at Rhinelander. We discuss major research programs and the scientists conducting this work, including (i) physiology of wood formation, (ii) short rotation intensive culture and short rotation woody crops, (iii) intensively cultured plantations, (iv) physiology and utilization of short rotation poplar yields, (v) breeding and selection, (vi) biotechnology and molecular genetics, (vii) atmospheric pollution and climate change, (viii) phytotechnologies, and (ix) ecosystem services. Also, we describe four major international conferences held in Rhinelander and (or) hosted by Rhinelander researchers.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".