Field pea response to seeding depth and P fertilization
Bibliographic record
Abstract
A study was conducted at Melfort, SK, in 1998 and 1999 to determine whether seeding depth and P fertilization affect field pea (Pisum sativum L.) seedling emergence and grain yield. Treatments included a factorial combination of three seeding depths (38 mm, 76 mm, and 114 mm) with and without 25 kg P2O5 ha–1 as monoammonium phosphate. While seedling density was not affected by seeding depth at 3 wk after planting, the deepest seeding depth produced significantly fewer seedlings than the two shallower depths at 5 wk post-seeding. A year-by-seeding-depth interaction was recorded for grain yield, with deep seeding (114 mm) reducing yield by 8.5% in 1998, while no significant differences were recorded due to depth in 1999. Side-banded phosphorus fertilizer applications reduced seedling emergence at 3 wk; however, no difference was recorded by 5 wk after seeding. At harvest, addition of P fertilizer significantly increased grain yields on this high P testing soil; however, this response was small, averaging 138 kg ha–1. Results of this trial indicate that while field peas can tolerate deep seeding there appears to be little benefit from seeding deeper than 76 mm. Key words: Field pea, seeding depth, P fertilizer
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 | 0.001 |
| 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".