1 Farmers and Consultants Receive Training in Spatial Analysis of Yield Monitor Data
Bibliographic record
Abstract
In response to suggestions from farmers and agribusiness, Purdue University offered a one-day workshop on spatial analysis of yield monitor data on March 1, 2007. There were 19 participants that included farmers from Indiana, Illinois, and Ontario, and representatives from agricultural input suppliers, farm cooperatives, and private consultants. The workshop was led by Terry Griffin, former Purdue University graduate student and currently Assistant Professor of Agricultural Economics and Agribusiness with the University of Arkansas Cooperative Extension Service. The discussions focused on strategies to design and implement on-farm experiments, collect data and harvest experiments, yield monitor calibration, precision agriculture, and spatial statistical analysis software. “Spatial analysis techniques recently adapted from other disciplines allow farmers and consultants to make better decisions based on their field-scale on-farm trial data collected with yield monitors than previously possible, ” Griffin said. “This workshop serves in part as a pilot program to pass the techniques developed from recent research over to the end users along with some basic statistical training. ” The agenda was largely set by the farmer-collaborators from
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.199 | 0.085 |
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".