The Effect of Biostimulation and Biostimulation-Bioaugmentation on Biodegradation of Oil-Pollution on Sandy Beaches Using Mesocosms
Bibliographic record
Abstract
To investigate a suitable biological remediation approaches for anticipating oil spills in Cilacap sandy beach (Indonesia), some alternative strategies using biostimulation and a combination of biostimulation-bioaugmentation have been evaluated in inter tidal near shore Cilacap, Indonesia. The purpose of the study was to compare the efficacy of biostimulation using slow release fertilizer (SRF) only, combination of biostimulation-single strain bioaugmentation, and combination of biostimulation-consortium bioaugmentation, to enhance oil degradation. The experiment was conducted using sediment polluted 100,000 ppm Arabian Light Crude Oil in a mesocosm system for 90 days. The parameters measured were oil depletion, bacterial growth and changes in environmental conditions. The results showed that the affectivity on oil depletion of biostimulation-bioaugmentation combination was observed faster and higher than biostimulation only. At the 16 th day application, the biostimulation with the added consortium and single strain treatment, increased oil depletion percentage by 2.2 and 1.6 times that of the control, respectively. For a longer period of treatment, both of combination treatments showed similar efficacy in degrading oil contamination in sandy beach. It is proposed that combination of biostimulation-bioaugmentation with the consortium is relatively better alternative for combating oil-pollution for a short period.
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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) | 0.001 | 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) | 0.000 | 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 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".