Bibliographic record
Abstract
Drought frequently limits cool‐season legume productivity during summer. Our objective was to determine the effect of drought (water deficits) on forage yield, forage quality, and stand persistence of Kura clover (Trifolium ambiguum M.B.), compared to alfalfa (Medicago sativa L.), red clover (Trifolium pratense L.), birdsfoot trefoil (Lotus corniculatus L.), and cicer milkvetch (Astragalus cicer L.). Kura clover was consistently among the lowest yielding legumes whether grown with (control) or without supplemental water applied while alfalfa was consistently among the highest yielding. Over 4 years, yields of Kura clover, alfalfa, red clover, birdsfoot trefoil, and cicer milkvetch were 168, 76, 117, 98, and 80% greater for the control compared to the drought treatment. Populations of Kura clover increased from 10 to over 35 plants/ft² during the experiment and were similar for drought and control treatments. Final populations of the other legumes averaged < 3 plants/ft². For both water regimes, Kura clover was consistently among those legumes with the highest herbage crude protein (CP) concentration and lowest fiber content. Drought decreased legume herbage neutral detergent fiber (NDF) and acid detergent fiber (ADF) concentration and increased NDF digestibility compared to the irrigated control. Kura clover will persist under drought, but its forage yields will be reduced to a greater extent than for other legumes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".