Cultivation of Saffron (<i>Crocus sativus</i> L.) in cold climates
Bibliographic record
Abstract
Saffron, an autumn crocus that produces a highly valuable spice, is grown mainly in Mediterranean climates. Nevertheless, saffron farms have been established recently in the province of Quebec. This led us to test cultivation practices that could influence plant phenology, saffron yield, and corm growth, including planting depth, planting period, and the application of fertilizers, mycorrhizal fungi, and biostimulants at planting. Soil temperature was monitored at the different planting depths throughout the year. Floral initiation was also monitored during spring and summer. Shoot emergence was delayed and final emergence reduced as planting depth increased; however, more shoots were produced by shallow-planted corms, which could lead to the production of corms too small to flower. The best time for planting saffron corm is between the end of July and the third week of August. Mineral fertilization hastened leaf emergence and improved corm production and their nutrient content. Neither the addition of mycorrhizal fungi or of biostimulants had any significant impact on saffron growth or flowering. Floral induction likely took place in July as flower bud appeared in early August. In most years, flower and saffron production was low in this location. It appears that soil temperature did not remain high for long enough during the summer to promote floral induction and autumn temperatures decreased too fast, limiting shoot and flower emergence most years. However, these climatic conditions did not affect corm production; corms could thus be sold to secure revenues for producers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".