Effects of diurnal temperature range and seasonal temperature pattern on the agronomic traits of fibre flax (Linum usitatissimum L.)
Bibliographic record
Abstract
Yang, F., Liu, F.-h. and Rowland, G. 2013. Effects of diurnal temperature range and seasonal temperature pattern on the agronomic traits of fibre flax ( Linum usitatissimum L.). Can. J. Plant Sci. 93: 1249-1255. Three cultivars of fibre flax (Linum usitatissimum L.), Viking, Argos and Ariane, were used to study the effect on various agronomic traits of diurnal temperature ranges (DIF) and seasonal temperature patterns (STP) applied throughout the crop growth period. Diurnal temperature ranges were set at 5, 10 and 15°C with the same daily mean temperature and accumulated growing degree days (GDDa), under a parabolic STP. Seasonal temperature patterns were set following parabolic, anti-parabolic and quasi-horizontal configurations with DIF set at 10°C. The results showed that DIF or cultivar had a significant influence on the biomass, plant height (PH), stem diameter (SD), length of technical stem (LTS), weight of technical stem (WTS), fibre weight (FW), fibre content (FC) and seed weight per pot (SWp). Seasonal temperature pattern also had a significant influence on these traits except for WTS and FW. Significant interactions were observed between DIF and cultivar, affecting all traits with the exception of FW, and between STP and cultivar, affecting biomass, PH, SD and WTS. Diurnal temperature range set at 10°C was optimal for biomass, PH, SD, LTS, WTS, and FW, while DIF set at 5°C benefited FC and SWp. Among the different STP configurations, anti-parabolic pattern was optimal for biomass, PH, SD, LTS and FC, and the quasi-horizontal pattern best fit SWp. In conclusion, the combination of DIF set at 10°C and anti-parabollic STP provided the most favorable temperature conditions for fibre flax growth, which is the situation with winter flax production in Yunnan, China.
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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) | 0.001 | 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".