Cultivation of Saffron (<i>Crocus sativus</i> L.) in cold climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it