Farm woodlots in northern New England, USA: Characteristics, management, and contributions to the whole farm system
Bibliographic record
Abstract
Abstract Farms and forests dominate the rural landscape of the northern New England states of Maine, New Hampshire and Vermont, among the most heavily forested states in the US. However, we know little about the stewardship of farm woodlots and their contributions to the whole farm system, despite region-wide increases in farm forest acreage. Using a mail survey, this study found that almost half of respondents had a written management plan for their forestland, most of which had been written by a forester, and approximately three-quarters took an active role in the management of their woodlots. Farm woodlot harvesting and management contributed over 7% of total farm income. Variables such as respondent's state of residence, age, education and type of farm were investigated in order to better understand farmers’ forest stewardship behavior. Implications for effective outreach to farm forest owners are offered.
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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".