Precision conservation in North America: Special section introduction
Bibliographic record
Abstract
Population growth and increasing demands on water resources make effective soil and water conservation essential to sustaining agricultural production and environmental quality . Berry et al. (2003) defined precision conservation as a set of spatial technologies and procedures to implement conservation management practices that integrates spatial and temporal variability across natural and agricultural systems. This definition integrates spatial technologies including global positioning systems, remote sensing, geographic information systems, and the capability to analyze and map these spatial relationships. Precision conservation is broader than precision agriculture since precision conservation contributes to soil and water conservation in agricultural and natural ecosystems. Berry et al. (2003; 2005) reported that precision agriculture focuses on maximizing yields, while precision conservation focuses on interconnected cycles and flows of energy, materials, chemicals, and water to reduce environmental impacts, off-site transport, and water pollution, while integrating practices that maximize conservation and productivity. The Berry et al. (2003) publication generated enough interest that the Soil Science Society of America, Canadian Soil Science Society, Mexican Soil Science Society and the Division of Soil Water and Management and Conservation organized and held a joint symposium titled “Precision Conservation in North America” at the November 1-4, 2004 annual meeting …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".