Thermotherapy via Humid Heat for the Treatment of Safflower Seeds
Bibliographic record
Abstract
The objectives of this work were to evaluate and adequate the method of thermotherapy via humid heat for the treatment of safflower seeds (Carthamus tinctorius L.) and, to verify its effect on the physiological and sanitary quality of seeds. The experiment was performed in the period from September to November, 2016 and from May to July, 2017, in entirely randomized design, arranged in 5 × 6 + 1 factorial scheme, with five temperatures: 25, 35, 45, 55 and 65 ºC and with six time periods: 5, 10, 15, 30, 45 and 60 min, plus the additional treatment (control), with eight repetitions. The seeds were packaged in glass of 500 mL and disposed in thermodigital water bath device with heated water according to the abovementioned factorial. We evaluated the degree of humidity of the seeds after thermotherapic treatments, the germination of normal seedlings, the emergence at field, the speed indexes of germination and emergence, the length and dry mass of seedlings and the sanity test. We observed that the treatment of seeds via humid heat thermotherapy was efficient in the control of phytopathogens, without damage to the physiological quality until 45 ºC. The treatment of 45 ºC for 15 min provided the greater reduction of the pathogens on the safflower seeds, incrementing its germinative potential and emergence at field.
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 | 0.000 |
| 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".