Using thermal time models to predict seedling emergence of orchardgrass (<i>Dactylis glomerata</i> L.) under alternating temperature regimes
Bibliographic record
Abstract
Abstract The effects of alternating temperatures on seed dormancy changes, germination and seedling emergence were investigated in ‘Arctic’ and ‘Lineta’ orchardgrass ( Dactylis glomerata L.). Thermal time models were successfully developed for 0, 5, 10 and 15°C temperature amplitudes, using 28 constant and alternating temperature regimes. These models were then modified by linking seed germination in Petri dishes and seedling emergence in soil. A field experiment was conducted with four seeding dates over 2 years to validate the modified thermal time models. Temperature regimes with a 5–15°C amplitude enhanced seed germination percentages of orchardgrass, indicating that the conditional dormancy was released by these temperature regimes. Base temperatures decreased with increasing temperature amplitude. Seeds germinated more rapidly under alternating temperatures than under constant temperatures. The dual effects of temperature for dormancy breaking and germination were accounted for by thermal time models based on alternating temperature regimes, which accurately predicted the timing and percentage of ‘Arctic’ and ‘Lineta’ orchardgrass seedlings emerging in the field ( R 2 ≥0.88).
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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 | 0.001 |
| 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".