Land use in Intermountain Conservation District, interactions between forestry and agriculture
Bibliographic record
Abstract
The landscape of the Intermountain Conservation District (IMCD) supports both agriculture and forestry activities. Recognizing the potential impact that can arise from these activities, the study was initiated to assess local impacts of forestry and agriculture on lands within the IMCD, illustrating the interaction of these activities on the landscape. Through interviews, local landowners identified land use impacts arising from forestry and agriculture, expressing opinion regarding the use of mitigative techniques and the effectiveness of regulation that directs forestry and agriculture on private and public lands. A watershed analysis of harvest and harvest mapping provided an illustration of where and what areas are being harvested. Land use mapping identified current trends in land use activity, particularly agriculture. The impact of water was the prominent impact of concern to residents. While acknowledging that agriculture does impact local water, public perception is that the expanding forestry occurring in the area will accentuate water-related problems that plague the area. Public concern regarding regulation of forestry on Crown land illustrates a need for improvement in local forest management by the government and Louisiana-Pacific Canada Limited. Increased education and regulation is needed to promote the protection of the local aquatic and terrestrial environments on private agricultural lands. The study recommends that water quality and flow be used as indicators of land use impacts. A joint framework to assess the use soil and later conservation techniques is advocated, providing the Intermountain Conservation District and Louisiana-Pacific Canada Limited with a mechanism to work together to ensure t e sustainability of local soil and water.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".