Predicting seed germination of slender wheatgrass [<i>Elymus trachycaulus</i> (Link) Gould subsp. trachycaulus] using thermal and hydro time models
Bibliographic record
Abstract
Schellenberg, M. P., Biligetu, B. and Wei, Y. 2013. Predicting seed germination of slender wheatgrass [Elymus trachycaulus (Link) Gould subsp. trachycaulus] using thermal and hydro time models. Can. J. Plant Sci. 93: 793–798. Slender wheatgrass [Elymus trachycaulus (Link) Gould subsp. trachycaulus] is a native caespitose grass used for forage production and reclamation. The objective of this study was to quantify seed germination requirements of slender wheatgrass using thermal and hydro time models. Slender wheatgrass, San Luis, had a base temperature (Tb) of 9.48°C, and required 946.8°C h to reach 50% of seed germination. Seed germination of San Lius occurred at a temperature range of 10–30°C, with the highest germination rate being achieved at 20°C, and the highest final germination percentage being achieved at 25°C. At 20 and 25°C, San Luis had a hydro time constant of 61 MPa h, and a median base water potential of approximately 1.0 MPa, but the germination had low uniformity in reduced water potentials. Final germination was reduced at or lower than –0.6 MPa. Compared with many other cool-season native grasses of Northern Great Plains, a relatively warm temperature would be necessary for uniform seedling establishment of this grass. In reclamation seeding, the seedling emergence could reach the highest level at a temperature of 25°C.
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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".