Predicting seed germination of winterfat (Eurotia lanata), a native forage species
Bibliographic record
Abstract
The timing of seed germination plays a critical role in the survival of plants in natural ecosystems. Population-based models for the prediction of seed germination as the function of temperature and water potential have been developed, which can also be used in predicting field emergence. We used winterfat (Eurotia lanata) to test variations in parameters of the thermal time and hydrothermal time model among seed mass classes and germination conditions. Germination rates (GR) of subpopulations were estimated from germination time courses over a water potential range from 0 to –1.33 MPa at 2, 5, 10, 15, 20, and 25 oC. Estimated base temperature (Tb) was lower in the large seed mass class (-4.5 oC) than the small seed mass class (-3.5 oC). The ζ b(50) was lowest at intermediate temperatures between 10 to 15 oC. A linear \nincrease of hydro time (ρH) with subpopulation was found at lower temperatures, especially at 2 oC. There were no significant differences in ζ b(50) between large and small seeds, but significant differences were observed in hydrothermal time requirement (ρHT(50)), which was lower at intermediate temperatures than at either lower or higher temperatures. The predictability of the thermal and hydrothermal time model was improved when parameters were allowed to change with seed size and germination conditions. Variations in Tb among seed mass classes favor large seeds, which accumulate more thermal time at a given temperature. This is particularly important for species such as winterfat, which germinates early in the season and early-emerged seedlings have better chance to establish and survive.
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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".