2014 Kansas Performance Tests with Corn Hybrids
Bibliographic record
Abstract
Corn performance tests, conducted annually by the Kansas Agricultural Experiment Station, provide farmers, extension workers, and seed industry personnel with unbiased agronomic information on many of the corn hybrids marketed in the state. Entry fees from private seed companies finance the tests. Because entry selection and location are voluntary, not all hybrids grown in the state are included in tests, and the same group of hybrids is not grown uniformly at all test locations. Most companies submit seed treated with systemic insecticides, which can affect yield in some situations. Contributors Main Station, Manhattan Jane Lingenfelser, Assistant Agronomist (Senior Author) Doug Jardine, Extension Plant Pathologist Jeff Whitworth, Extension Entomologist Mary Knapp, KSU Weather Data Librarian Edward O. Quigley, Agricultural Technician Experiment Fields Eric Adee, Topeka Gary Cramer, Hutchinson James Kimball, Ottawa Michael Larson, Scandia Wendell Lilyhorn, Hutchinson Randall Nelson, Scandia Keith Thompson, Hutchinson Research Centers Wayne Aschwege, Hays DeWayne Bond, Tribune Patrick Evans, Colby Kelly Kusel, Parsons Alan Schlegel, Tribune Monty Spangler, Garden City Cooperators Gene Eidman, Strong City Fuhrman Farms, Severance Lance Rezac, Onaga Norman Schmidt, Inman Clayton Short, Assaria Justin Vosburgh, Macksville
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".