Meta-analysis of lettuce (<i>Lactuca sativa</i> L.) response to added N in organic soils<sup>1</sup>
Bibliographic record
Abstract
A soil test N (STN) is required to implement N fertilizer recommendations for vegetable crops in cultivated organic soils. A STN can be developed using the isometric log ratio (ILR) to balance soil C, N, and other soil components amalgamated into a filling value (Fv) and to derive a Mahalanobis distance ([Formula: see text]) as STN. Our objective was to conduct a meta-analysis of multi-year and multi-site N trials on lettuce (Latuca sativa L.) response to added N along a gradient of [Formula: see text] values, and to develop an N recommendation model. Twenty-four N fertilization experiments were conducted from 2002 to 2006 in organic soils of southwestern Quebec. Each crop received four N rates from zero to 120–150 kg N ha−1 applied before seeding or in split applications. The relationship between N requirements by lettuce and STN was quadratic whether lettuce was seeded or planted. There were three STN fertility classes delineated by [Formula: see text] values of 1 and 5.5. The N recommendation model and its uncertainty were valuable for [Formula: see text] values up to 8.4. This approach provides a reliable test soil N to implement appropriate fertilizer recommendations for lettuce in organic soils.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".