7. Effects of Farming in Biodiversity
Bibliographic record
Abstract
Recent shifts in agricultural practices to become more intensive have led to habitat degradation and the displacement of native flora and fauna, reducing the biodiversity associated with farmlands. Biodiversity is important for the health of the ecosystem in the affected area and our project aims to investigate modern agricultural practices to determine which aspects are responsible for the decline in biodiversity and what is threatening the recovery of endangered species. Direct effects of cultivation practices, indirect effects of pesticide use, and farmers’ perspective on the value of wildlife will all be investigated for their potential link to biodiversity declines. By working with the Ontario Federation of Agriculture, and local farmers, we hope to come up with feasible recommendations to improve levels of biodiversity. These recommendations will focus on altering methods of agriculture to increase the diversity of species on farmland and to help the recovery of species at risk including the birds bobolink, and eastern meadowlark, as well as the milk snake, to name a few. The aim of these recommendations is to not only improve habitat for wildlife, but to provide the farmers involved with beneficial ecosystem services that are more sustainable than current practices. By doing so, the presence of wildlife could be seen as a valuable contribution to their operation, not as a burden to productivity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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".