Phytosociological Survey of Weeds on Degraded and Well-Managed Pastures: Agronomical and Ecological Implications
Bibliographic record
Abstract
The objective of this research was to carry out a survey of weeds in pastures in the Middle Valley of Paraíba do Sul, Rio de Janeiro State, Brazil, in order to subsidize weed management and pasture recovery. Weed identification and plant count were carried out in pastures with four levels of degradation, classified as low (N1), moderate (N2), strong (N3) and very strong (N4), with five replications. Thirty-nine weed species were identified and distributed into16 botanical families. Poaceae, Asteraceae and Fabaceae were the most relevant families. The number and density of weeds increased as the level of degradation decreased. The relative importance of weed species varied with the level of degraded pasture. The main weeds found in N1 were Melinis minutiflora, Desmodium incanum, Croton lundianus, Andropogon bicornis, and Imperata brasiliensis; in N2: Paspalum notatum, Melinis minutiflora, Imperata brasiliensis, Sida rhombifolia, and Desmodium incanum; in N3: Paspalum notatum, Melinis minutiflora, Sida rhombifolia, Eupatorium maximilianii, and Imperata brasiliensis; in N4: Paspalum notatum, Melinis minutiflora, Cynodon dactilon, Eupatorium maximilianii, and Imperata brasiliensis. The similarity index was high, showing the homogeneity of weeds among areas. The predominant species, considering all areas, were in increasing order of importance: Cynodon dactilon, Melinis minutiflora and Paspalum notatum. Decision-making about applying control measures could be marked out when the plant density reached out or exceed the average of 3.58 plants m-2.
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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 | 0.001 | 0.001 |
| 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".