Growth and nutrient accumulation metrics of <i>Diodia saponariifolia</i> plants as a potential native cover crop in southeastern Brazil
Bibliographic record
Abstract
Abstract Competition for resources between crops and weeds hinders the increase of production in agroecosystems. The trait‐based plant species selection of cover crops can be a useful tool to suppress competing plants in addition to providing environmental services. Here, we assessed the growth and macronutrient accumulation metrics in Diodia saponariifolia (Rubiaceae) plants, a native cover crop found in family farming systems in southeastern Brazil. Under greenhouse conditions, three viable D. saponariifolia cuttings were planted per tray. The experimental design was entirely randomized, with treatments consisting of plant sampling times, at regular intervals of 7 days between 16 and 93 days after transplanting (DAT) and 15 days between 93 and 138 DAT. Based on dry mass and chemical analysis of leaves, stems, and roots; we fitted the logistic model to explore the metrics of growth and macronutrient accumulation. Overall, the increment in plant dry mass was slow about halfway through the experimental period, being subsequently replaced by a phase of rapid growth. The stem was the plant fraction with the highest relative biomass accumulation. The total macronutrient concentration followed the descending order of K > N > Ca > Mg > P, varying along the plant ontogeny. Considering the nutrient content, the estimated 200 g m−2 of aboveground dry mass, and 0.3 m2 of leaf area, it is suggested to perform the first mechanical weeding along the third month of growth. Our results suggest that D. saponariifolia has satisfactory agronomical features for its establishment as a cover crop in different agricultural contexts.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".