Symposium no. 14 Paper no. 27 Presentation: poster 27-1
Bibliographic record
Abstract
A number of field experiments were conducted from 1998 to 2001 (or are underway) in northeastern Saskatchewan to determine the effects of various rates (0 to 30 kg S ha ), sources (sulphate S-potassium sulphate, ammonium sulphate, potassium thiosulphate and ammonium thiosulphate; and elemental S-ES 90 and ES 95), times (autumn, sowing, bolting and flowering) and methods (incorporation, sideband, seedrow, topdress and foliar) of S application, ratios of fertilizer N:S (0 to 150 kg N ha ) and cultivars (Quantum-Brassica napus, AC Excel-Brassica napus, Maverick-Brassica rapa and AC Parkland-Brassica rapa) on seed yield and quality of canola. The S deficiency in canola can be corrected and seed yields restored with application of sulphate-S fertilizer in the growing season, substantially until bolting growth stage and moderately at early flowering stage. There was no significant increase in seed yield from the elemental S fertilizers in the initial year of application. Even after three annual applications, the elemental S fertilizers had seed yields lower than the sulphate-S fertilizers in many cases particularly when the S fertilizers were applied in spring. Autumn-applied elemental S had greater seed yield than the springapplied elemental S. For higher N application rates, there is a need of increased amount of fertilizer S to adequately meet the S requirements of canola. The severity of S deficiency, increase in seed yield of canola from applied S and seed quality varied with canola cultivars. In general, Quantum had the highest seed yield, followed by AC Excel, Maverick and AC Parkland. Application of S fertilizer also increased oil content in canola seed. In conclusion, seed yield and quality of canola can be optimized with proper fertilizer management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.495 | 0.286 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".