You want me to grow trees? The social implications of biomass crops on the resilience of Quesnel, British Columbia.
Bibliographic record
Abstract
Forest-dependent communities in the central-interior of British Columbia are facing an increasingly uncertain future due to ongoing change in the forest industry and more recently because of the mountain pine beetle. The use of forest resources to produce biochemical and bioproducts is seen by many communities and the provincial and federal governments as having potential to help communities adapt to this change. This thesis will focus on the implications of establishing short-rotation forestry crops on marginal ranchland in Quesnel, BC to provide a source of fibre for a biochemical/bioproducts industry. Ranchers in the Quesnel area were interviewed to determine how short rotation forestry crops would affect their current operations, the opportunities and barriers they see to its implementation, and their level of interest in growing these crops on their own land. The majority of ranchers were willing to grow crops provided it was profitable, though they were concerned about losing agricultural land to tree production. If agroforestry methods that integrate traditional crop production with short rotation forestry crops are used, there is potential for ranchers to have another source of income. However, there is also a chance that agricultural land will be removed from food production or excessively regulated. Both of these outcomes would negatively affect ranchers. --Leaf 3.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".