Ecogeographic Factors Affecting Inflorescence Emergence of Cool‐Season Forage Grasses
Bibliographic record
Abstract
The ability to predict when a cool‐season forage grass cultivar will begin inflorescence emergence under different ecogeographical conditions would allow plant breeders, agronomists, and grass‐seed marketers to better position that cultivar into a forage production system. Our objective was to determine the ecogeographical factors (longitude, latitude, elevation, day of year when average daily temperature exceeds 0°C for five consecutive days [DOY at 0°C], cumulative growing degree‐day [GDD], photoperiod, and cumulative photosynthetic active radiation [PAR]) that have the greatest effect on grass maturation in the spring. Inflorescence emergence was monitored in established cultivars of festulolium (× Festulolium spp.), orchardgrass (Dactylis glomerata L.), ryegrass (Lolium perenne L. and Lolium multiflorum Lam.), tall fescue (Festuca arundinacea Schreb.), and timothy (Phleum pratense L.) at eight locations in North America during the spring of 2004 and 2005. As latitude increased, the day of year when grasses reached 1% inflorescence emergence (DOY) also increased, while cumulative GDD and PAR decreased. Latitude, cumulative PAR, and DOY at 0°C were more closely correlated (r2 ≥ 0.67) to the onset of inflorescence emergence than the other variables. Latitude combined with the inverse transformation of PAR provided the best prediction of when these grasses would initiate inflorescence emergence (validation R2 for all species ≥ 0.83).
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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) | 0.000 | 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".