Environmental Adaptability and Rock Dust Concentrations in Lettuce Cultivars
Bibliographic record
Abstract
The purpose of this work is to analyze lettuce cultivars different responses to environmental adaptability and rock dust concentrations in agroclimatic conditions in the south-west region of Goiás state. The work was conducted in the county of Mineiros, Goiás. The experimental area soil’s was classified as quartzarenic NEOSOL. In experiment number 1, was used experimental design in random blocks in factorial 5 × 2, corresponding to five rock dust concentrations (0, 100, 200, 300 and 400 kg ha-1), in two lettuce cultivars of Crespa and Americana lettuce. In experiment number 2 was applied experimental design in randomized blocks, which were constituted by 7 lettuce cultivars (Hanson, Simpson S. Preta, Baba de Verão, Maravilha de Inverno, Grandes Lagos, Crespa Palmas, and 4 Estação). The data results were analyzed 45 days after seeds transplant. The results were submitted to variance analysis and Turkey’s regression and test at a 5% probability. The 400 kg ha-1 rock dust dose didn’t have any effects in lettuce cultivars Crespa and Americana, once that, rock dust nutrients mineralization occurs very slowly, not interfering in the lettuce first cycle. 4 Estação cultivar presented good environmental adaptability to Goiás south-west agroclimatic conditions, more specifically in Mineiros, where it is recommended lettuce cultivation in summer-fall.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".