A critical physical geography of no‐till agriculture: Linking degraded environmental quality to conservation policies in an Oregon watershed
Bibliographic record
Abstract
A variety of agricultural conservation trends have gained and lost favour throughout the years, with farm bills in the United States often influencing which conservation practices are implemented. This paper explores the consequences of a set of conservation techniques loosely defined as “no‐till agriculture,” focusing on their implementation and adoption since 1985, at which point such approaches began to be explicitly encouraged under US Farm Bill soil conservation mandates. We begin by noting a core contradiction that has characterized these approaches in the Fifteenmile Watershed of Wasco County, Oregon, where despite high rates of farmer enrollment in no‐till programs, both no‐till agriculture and sustained tillage have led to the increased use of herbicides and sustained sediment runoff. Using a critical physical geography framework that integrates intensive physical field data collection, spatial analysis, social surveys, and interviews, we address the biophysical and social factors collectively driving changes in herbicide use and variable erosion estimates. We draw particular attention to how farm bill support for no‐till has enrolled farmers in a vaguely defined and underregulated conservation practice that may ultimately undermine environmental quality .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".