Growth of Alfalfa in Sludge‐Amended Soils and Inoculated with Rhizobia Produced in Sludge
Bibliographic record
Abstract
The efficiency of rhizobial inoculants produced in wastewater sludge used as a growth medium and as a carrier was compared with that of inoculants produced in yeast mannitol broth (YMB) medium and by using peat as a carrier. Alfalfa (Medicago sativa L.) plants were inoculated with solid and liquid Sinorhizobium meliloti inoculants and grown in pots containing two soil types (Kamouraska clay soil and Saint-André sandy soil). The effect of various levels of sludge amendment (60 and 120 kg N/ha) and nitrogen fertilizer (60 kg N/ ha) was also studied. The sludge-based inoculants showed the same symbiotic efficiency (nodulation and plant yield) as YMB-based inoculants. The inoculation increased the nodulation indexes from 4-6 to 8-12, and the rhizobial number from 10(3) (uninoculated soils) to 10(6)-10(7) cells/g in inoculated soils. However, the shoot dry weights and the nitrogen contents were not increased significantly by the inoculation. Applying sludge as an amendment enhanced the rhizobial number in soils from 10(3) to 10(4) cells/g and improved significantly the plant growth (shoot dry weights and nitrogen contents). This improvement increased with sludge rate and with the cut (three cuts). Compared with sludge, N fertilizer gave lower plant yields. The nodulation was not affected by sludge and N-fertilizer application. The texture and physico-chemical properties of soil were found to affect the yield and nitrogen content of the plants. In this study, macroelements and heavy metals were at acceptable levels and were not considered to be negative factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".