Interactive effects of temperature, carbon dioxide and watering regime on seed germinability of two genotypes of<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Abstract We examined the combined effects of temperature, carbon dioxide (CO 2 ) and watering regime during seed maturation on subsequent germinability and total phenolics of Arabidopsis thaliana [wild-type (WT) and abi1-1 mutant] seeds. Mature seeds were collected from plants that were grown under lower (22/18°C, 16 h light and 8 h dark) or higher (28/24°C, 16 h light and 8 h dark) temperatures, at ambient (400 μmol mol –1 ) or elevated (700 μmol mol –1 ) CO 2 concentration, and well-watered or water-stressed. Germinated and non-germinated (viable, rotten and empty) seed percentages, germination rate and total phenolics were determined for both genotypes. Higher maturation temperatures increased seed germination percentage, but decreased germination rate, percentage of rotten and non-germinated viable seeds, and total phenolics. Elevated CO 2 increased seed total phenolics. Water stress decreased the percentage of non-germinated viable seeds. Neither of the two latter factors affected other measured parameters. Seeds of the abi1-1 mutant had higher total phenolics. The fate of seeds was mostly affected by higher temperatures and water stress. Also, seeds of the abi1-1 mutant had higher germination rate, empty seed percentage and total phenolics than seeds of the WT genotype. Germination percentage was highest for the WT seeds that matured on the water-stressed plants that were grown under higher temperatures at ambient CO 2 . It can be concluded that higher temperatures had highest effects on seed germinability and other parameters, and elevated CO 2 did not alleviate the negative effects of higher temperatures on seed viability.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".