Slow-release nitrogen fertilizer in carrot production on Prince Edward Island
Bibliographic record
Abstract
Sanderson, K. R. and Fillmore, S. A. E. 2012. Slow-release nitrogen fertilizer in carrot production on Prince Edward Island. Can. J. Plant Sci. 92: 1223-1228. The impact of nitrogen management using slow-release fertilizers has not been examined for carrot (Daucus carota L.) production in Atlantic Canada. To assess the effects of such products, we evaluated five slow-release fertilizers over a 3-yr period. Treatments consisted of sulphur coated urea (SCU) (42-0-0), isobutylidene diurea (IBDU) (31-0-0), Nutralene® (methylene urea) (40-0-0), Sirflor® (urea formaldehyde) (38-0-0), UFLEXX™ (urea, dicyandiamide, N-(n-butyl) thiophosphoric triamide) (46-0-0) compared with industry standard (ammonium nitrate) (34-0-0). All slow-release fertilizer treatments supplied N at 50 kg N ha-1 pre-plant. Slow-release fertilizer treatments were compared with the industry standard of 50 kg N ha-1 pre-plant plus 50 kg N ha-1 as a top dress. UFLEXX™ increased biological and marketable yield by 7.5 and 18.7%, respectively, compared with the industry standard. Sirflor® produced the lowest yield. GreenSeeker® NDVI reading and LECO N were correlated at the mid-season sample. GreenSeeker® technology has potential as an in-field crop health assessment tool, however, further research is required to develop GreenSeeker® algorithms appropriate for carrot production in Prince Edward Island.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".