Parameters of Physiology, Nutrition and Quality of Eugenia dysenterica DC Seedlings Grown in Organic Substrates from the Agricultural Industry
Bibliographic record
Abstract
Residues from agricultural activities might be used as substrates for production of seedlings. In this study it was aimed to evaluate the physiology, nutrition and quality of Eugenia dysenterica DC. seedlings grown in substrates derived from agricultural organic wastes. The wastes used were as follows: rice husks (RHs); fermented cattle manure (FCM); cattle manure compost (CMC), which contained corn silage and FCM; sugarcane bagasse (SCB); filter cake from sugar-alcohol mills (FC); and subsoil (SB). Four substrates were formulated from the wastes, namely SB+RH (1:1; v:v), SB+CMC (1:1), SB+FCM (3:1) and SCB+FC (3:2) and BioplantÒsubstrate was also used. The following characteristics were evaluated: the emergence and vigor percentages, biometric characteristics, seedling quality indices, gas exchange, chlorophyll a fluorescence and leaf nutrient levels. The seedling emergence was similar for all the substrates. The highest vigor was found in seedlings that were cultivated in SCB+FC, which was equal to the vigor observed in BioplantÒ. In general, the use of the SB+CMC substrate gave better seedling results for the biometric and nutritional characteristics, followed by SCB+FC. Most of the biometric characteristics showed a correlation with photosynthesis, electron transport rate and effective quantum yield of photosystem II, thus showing that these characteristics can be alternatives to the traditional quality indexes used for seedlings. The use of CMC, SCB and FC was shown to be appropriate for the production and nutritional supply of seedlings’ specie. The composition SB (subsoil)+CMC (1:1) proved to be the most suitable to produce E. dysenterica seedlings.
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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".