Water and Nutritional Management on the Growthand Chlorophyll a Fluorescence of Plants Used in the Revegetation of Remaining Sand and Clay Extraction Areas
Bibliographic record
Abstract
The processes of using the environment and natural resources are increasingly necessary and present in human society. These processes can result in environmental degradation. A recovery strategy for an area that has undergone environmental degradation is revegetation. For the successful establishment of a plant species, the environment must have adequate water and nutritional conditions. The objective of this work was to study the effect of water and nutritional management on the survival, growth, and morphophysiological conditions of plants used in the revegetation of remaining sand and clay extraction areas. The experiment was carried out in a sand loan extraction loan area and a clay loan extraction loan area, both in the coastal region of the municipality of São Mateus, Espírito Santo, Brazil. The experimental design was a randomized block with three replications in a split-plot scheme, using methods of water management in the plots and doses of fertilization (0.000 kg, 0.072 kg, 0.144 kg, 0.288 kg and 0.576 kg) in the pits in the subplots. In both areas, five different species of native plants were used: Aroeira (Schinus terebinthifolius Raddi), Cajá Mirim (Spondias mombin L.), Goiaba do Ipiranga (Psidium cattleianum Sabine), Ingá Mirim (Inga laurina (Sw.) Willd.) and Murta de Restinga (Mouriri guianensis Aubl.). The plants used in the experiment were evaluated for growth, survival, leaf attributes, and chlorophyll a fluorescence. The water management method and the fertilization of the pit had a significant effect on the development of the species evaluated in both areas, acting on the survival rate, growth, morphology and physiology of the plants.
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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) | 0.001 | 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".