Composted sewage sludge as an alternative substrate for forest seedlings production
Bibliographic record
Abstract
The production of forest seedlings with adequate morphological and physiological characteristics is essential for the success of plantations. Substrates and irrigation are the major factors determining seedlings’ growth. Substrates made of urban and agricultural residues are a sustainable alternative to peat-based substrates. In this study, we evaluated how composted sewage sludge substrates affect the growth and gas exchange in seedlings of Cedrela fissilis Vell. Seedlings were produced under daily irrigation depths of 6, 9, and 12 mm, and on different substrates. The substrates were based on sewage sludge composted with Eucalyptus bark or sugarcane bagasse, and a commercial substrate based on peat, involving a double factorial design with 12 treatments (3 irrigation depths × 3 substrates). Both physical and chemical characteristics of substrates were analyzed, and morphological traits and gas exchanges of seedlings were measured. Sewage sludge-based substrates presented different characteristics according to the material it was mixed. Eucalyptus bark provided higher bulk density (0.19 g cm-3) and lower total porosity (75%) to the substrate, while sugarcane bagasse increased macroporosity up to 60%. Seedlings produced in sewage sludge-based substrates presented a height up to 17.8 cm and stem diameters of between 8.39-10.29 mm. Higher shoot and root dry mass was obtained in sewage sludge-based substrates with irrigation depth of 9 mm, which were 3.71 and 2.01 g, respectively. Photosynthetic carbon assimilation varied between 2.26 and 3.23 µmol CO2 m-2 s-1, and water use efficiency varied from 2.058 to 3.395 µmol CO2 (mol H2O)-1, with the highest values being obtained in seedlings produced in sewage sludge-based substrates with irrigation depth of 6 mm. Our results demonstrate that sewage sludge-based substrates are an efficient alternative to commercial peat-based substrates for seedling production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".