2009 Kansas performance tests with grain sorghum hybrids
Bibliographic record
Abstract
Grain Sorghum Performance Tests, conducted annually by \nthe Kansas Agricultural Experiment Station, provide \nfarmers, extension workers, and seed industry personnel \nwith unbiased agronomic information on many of the grain \nsorghum hybrids marketed in the state. Because entry \nselection and location are voluntary, not all hybrids grown in \nthe state are included in tests, and the same group of hybrids \nis not grown at all test locations. \n \nContributors \nMain Station, Manhattan \nJane Lingenfelser, Assistant Agronomist (Senior Author) \nDoug Jardine, Extension Plant Pathologist \nJeff Whitworth, Extension Entomologist \nMary Knapp, KSU Weather Data Librarian \nEdward O. Quigley, Agricultural Technician \nExperiment Fields \nEric Adee, Topeka \nGary Cramer Hutchinson \nJames Kimball, Ottawa \nWendell Lilyhorn, Hutchinson \nRandall Nelson, Scandia \nKeith Thompson, Hutchinson \nResearch Centers \nWayne Aschwege, Hays \nPatrick Evans, Colby \nKelly Kusel, Parsons \nAlan Schlegel, Tribune \nMonty Spangler, Garden City \nCooperators \nScott Chapman, Beloit \nClayton Short, Assaria
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".