Bibliographic record
Abstract
Hailstorms can be responsible for significant economic loss to the agricultural sector in Alberta. Foliar applications of certain fungicides and nutrient blends have been advocated to promote recovery and yield of hail-damaged crops. Proper understanding of different crop- and hail-related factors is required for an accurate assessment of hail damage to crops, and for evaluations of hail-recovery product claims. This study was undertaken at three locations in Alberta during three growing seasons (2016-18) to determine the effect(s) of two levels of simulated hail severity at three different crop developmental stages including early vegetative, middle vegetative and reproductive stages. Plant growth, yield and grain quality parameters of wheat, field pea and dry bean crops were measured. Simulated hail damage led to reductions in crop height, biomass, canopy cover, grain yield and kernel weight of all three crops. Timing of simulated hail was a critical factor influencing the extent of crop damage with early damage to vegetative stages having less effect on yield compared to damage at later reproductive stages. This trend was especially evident in wheat which did not show significant reduction in yield from the damage at early tillering stages but had significant yield loss with damage at flowering. Foliar applications of fungicides and nutrient blends did not significantly improve crop recovery, grain yield or kernel weight for any of the crops in this study, and thus, their use for the recovery of hail-affected wheat, field pea and dry bean was not supported by the results of this study. Link to Video Presentation: https://youtu.be/vgUDIufNwPo
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".