THE TECHNIQUE OF TOBACCO SEEDLING NURSING ON MOIST TRAYS AND ITS APPLICATION I.MEDIA FORMULA SELECTION AND SEEDLING COMPARISON
Bibliographic record
Abstract
In order to solve the problems existing in the present floating system for tobacco seedling raising in water beds, a new technique was investigated where the seedlings were kept in moist trays while nutritional solution was applied overhead. The results at four experiment locations indicated that compared with the float system, the temperature in the media was, on the average, 1.3℃ higher, seed germination started 2 days earlier, germination rate was raised by 5.2%, fewer blue-green algae occurred on the surface of the media, dry matter weight per plant was 17.6% higher, root/shoot ratio was 17.7% higher and the cost for seedling nursing was significantly lower. Five promising fomulae of the media were selected, compared in a further experiment with the media imported from Canada and two best media were identified, which were shown to have good physico-chemical properties and gave satisfactory nursing results, and were, therefore,recommended for use in place of the imported media.
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 | 0.001 | 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".