Integrating production functions of timber, forage, and cattle to achieve sustainable resource management.
Bibliographic record
Abstract
In the Peace Forest District (PFD) of British Columbia (BC), Canada, approximately 40 percent of forest tenures contain overlapping licenses to graze under the Range Act on Crown range in broadleaf and mixedwood forest types. Resource conflict, over land usage, will increase because of more utilization of broadleaf and mixedwood stands in the PFD. Aspen (Populus tremuloides Michx.] harvesting and reforestation appears to have the greatest impact on forage availability to domestic livestock areas through significant reductions in cattle access as a result of aspen regeneration. Geographical Information System (GIS) analysis and ground-truthing were done on three PFD community pastures. Two areas were surveyed during the 2002 - 2005 grazing seasons while the third was surveyed in 2004 and 2005. For the sites monitored over four years the cattle spent about 60 percent of their time in the same habitat types concurrently including roadways, tame pasture, and mature 'park-like' aspen stands. This exacerbates potential conflict between the cattle, forest industries, and other resource development industries such as oil and gas development. --P.ii.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".