Quantification of camelina germination niche to combined salinity and temperature stresses using a halothermal time model
Bibliographic record
Abstract
The present work was carried out to quantify camelina germination niche under salinity and temperature ( T) stress using halotime and halothermal time models. Seed germination ( SG) time courses were recorded at six constant temperatures (5 °C, 10 °C, 15 °C, 20 °C, 25 °C, and 30 °C) and at each of the following levels of NaCl concentrations (0, 50, 100, 150, 200, and 250 mM). Halotime model could well describe the germination time courses across a range of NaCl concentrations ( R2 = 0.79–0.91). The median base NaCl concentration was approximately constant (231 mM) at suboptimal T followed by a linear decrease, with the rate of 17.4 mM °C−1, under supra-optimal T. However, the germination rate increased with increasing T, reaching its maximum at a fixed optimum temperature ( To = 20 °C), independent of NaCl concentration. Then it decreased linearly for T > To and intercepted the x-axis at the ceiling temperature values of 33.5 °C, 30 °C, 28 °C, 25 °C, and 22 °C under 0, 50, 100, 150, and 200 mM NaCl, respectively. Consequently, based on model estimation the adequate soil T for SG of camelina should be in the range of 0.4 °C–33.5 °C, and the To is 20 °C. Besides, the salinity should be less than 231 mM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".