Using Previously Reported Cropland Acreage in Data Collection
Bibliographic record
Abstract
The National Agricultural Statistics Service (NASS) conducts surveys on the United States’ and Puerto Rico’s agriculture for the purpose of estimating crops, livestock, production practices, and farm economics. NASS’ Research and Development Division examined the agency’s June and September Agricultural Surveys to determine whether identical questions asked on both surveys could be eliminated on the September survey for June respondents, thereby reducing respondent burden. The questionnaires for these two surveys for five states (Delaware, Iowa, Nebraska, Texas, and Washington) were reviewed and identical questions (cropland, land owned, land rented to, land rented from, and storage capacity) were identified. This report specifically focuses on cropland. A second report focusing on additional items is scheduled for late 2009. Initially, between-quarter differences in cropland for the same operation were measured. If the indicated variation in the answers was not statistically significant, it was concluded that the questions could be omitted in September for those operators responding in June. This initial test showed that four of the five test states could have the June cropland data carried forward to the September Agricultural Survey. The analyses were then expanded to include all states participating in the June and September Agricultural Surveys. The results indicated that if the quality of the cropland data collected in June is improved then using the June reported data in September is feasible in the following twenty-two states: Arkansas, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Louisiana, Maine, Michigan, Minnesota, Mississippi, Montana, Nebraska, North Dakota, Ohio, Pennsylvania, South Dakota, Utah, Virginia, Washington, and Wisconsin.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".