Biomass Production and Antioxidative Enzyme Activities of Sunflower Plants Growing in Substrates Containing Sediment from a Tropical Reservoir
Bibliographic record
Abstract
Many Brazilian reservoirs are intensely submitted to the silting process, particularly the small and medium size ones. The study aimed to examine the feasibility of using silt sediment to grow sunflower plants under conditions of water stress, by evaluating its effects on the relative chlorophyll contents, dry matter and antioxidative enzyme system. The study was conducted under greenhouse conditions at the Instituto Federal do Ceará Campus Maracanaú, Brazil. The sunflower seeds were sown in buckets containing 1) sand; 2) sand + manure/mixed organic fertilizer; 3) sand + 91.8 g of sediment, and 4) sand + 183.6 g of sediment. The sediment was collected from the Tijuquinha reservoir, Northeast of Brazil. The plants were watered daily to 70% field capacity. At 16 days after sowing, irrigation to half of each group of seedlings was suspended. The experimental design was completely randomized in a 2 × 4 factorial with five replicates. The data of each harvest time were analysed by analysis of variance and the means were compared by Tukey’s test (P ≤ 0.05). The addition of silt sediment improved the variables (relative chlorophyll content, and shoot and total dry matters) compared to plants grown in substrate containing sand and sand + compost/mixed organic fertilizer, respectively. In general, a greater increase in the variables was observed with the 200% nitrogen recommendation treatment than the other treatments studied. It is possible that the silt sediment from reservoirs can be an alternative to chemical fertilizers for plant cultivation, reducing production costs, providing improvements in the quality of potable water and restoring the storage capacity of surface reservoirs lost by siltation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".