Yield and quality of forage maize grown under marginal climatic conditions in Northern Ireland
Bibliographic record
Abstract
Abstract The expansion of maize (Zea mays) into cooler areas has been facilitated by the availability of early maturing cultivars and by cultivation under plastic mulch. However, year‐to‐year variation in harvest quality remains a problem. The yield and quality of ‘Goldcob’, an early maturing forage maize, were assessed over 5 years from plots grown in the open and under plastic mulch. For both treatments, there was significant between‐year variation in yield and quality (starch content, metabolizable energy, organic matter and D‐value), and starch content was particularly variable. The use of plastic mulch to warm the soil advanced the establishment of the crop, with silking occurring on average 19 days earlier. This resulted in significantly higher yields under plastic mulch, with a mean increase of 3·9 t ha−1. The plastic mulch also resulted in significant increases in quality parameters, with starch content showing a mean increase of 36%. The Ontario heat unit model explained much of the variability in yield, both in the open and under plastic mulch. Plastic mulch had no consistent effect on Ontario heat units accumulated prior to silking, but Ontario heat units accumulated between silking and harvest (OHUpost‐silk) were found to be an adequate predictor of yield. The response of starch content was more complex, showing a clear plateau in the response to temperature at 1200 OHUpost‐silk, above which the accumulation of starch appears limited by other factors.
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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".