Effects of the humic substances of de-inking paper sludge on the antagonism between two compost bacteria and Pythium ultimum
Bibliographic record
Abstract
We investigated the in vitro influence of humic substances (HS) extracted from de-inking paper sludge compost on the inhibition of Pythium ultimum by two compost bacteria, Rhizobium radiobacter (Agrobacterium radiobacter) and Pseudomonas aeruginosa. When low concentrations (5 or 50 mg l(-1)) of HS were added to the culture medium, fungal inhibition by R. radiobacter significantly increased (P<0.01) by 2-3%. In contrast, these low levels of HS had no effect on P. ultimum inhibition by P. aeruginosa. The Fe, chelated by HS, was in part responsible for the decrease of P. ultimum inhibition by the bacteria when increasing amounts of HS were added in the culture medium. The addition of 500 mg l(-1) of humic acids isolated from de-inking paper sludge compost or from fossil origin completely eliminated the inhibition of P. ultimum by R. radiobacter. This Fe effect also stimulated growth of R. radiobacter and reduced its siderophore production in a minimal medium supplemented with HS as sole source of Fe. The results showed that HS influence microbial antagonism when added to a culture medium. However, this effect varies with different factors such as the type of bacteria, concentration of HS, molecular weight and Fe content.
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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) | 0.000 | 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".