Taking the Guesswork Out of Lodging Ratings
Bibliographic record
Abstract
Taking the Guesswork Out of Lodging RatingsLodging results from interactions among the crop canopy, environmental conditions, and the soil, making it unpredictable and difficult to study in small-plot research.Destructive plant measurements can give an indication of lodging risk but are time consuming and expensive for research trials with multiple objectives.A device that could quickly and non-destructively measure lodging risk would be a welcome addition to any agronomic researcher's toolbox.In an article recently published in Agronomy Journal, researchers evaluated the ability of a push-force meter known as "the Stalker" to indicate both shoot-and root-lodging risk in spring wheat grown under a range of agronomic management.By measuring stem strength and elasticity, the Stalker was able to identify agronomic practices with high and low lodging risk.Applying a plant growth regulator increased stem strength while reducing plant densities increased both stem strength and elasticity.Led by the University of Manitoba, this research demonstrates the ability of the Stalker to differentiate high-and low-lodging risk agronomic practices without having natural or artificially induced lodging.The work delivers a standardized DoI: 10.1002/csan.20962method for evaluating lodging risk in research trials, removing the reliance on environmental conditions.
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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 | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".