Peer Review #2 of "Bioremediation of engine-oil contaminated soil using local residual organic matter (v0.1)"
Bibliographic record
Abstract
Soil remediation industries continue to seek technologies to speed-up treatment and reduce operating costs.Some processes are energy intensive and, in some cases, transport can be the main source of carbon emissions.Residual fertilizing matter (RFM), such as organic residues, have the potential to be beneficial bioremediation agents.Following a circular economy framework, we investigated the feasibility of sourcing RFMs locally to reduce transport and assess possible bioremediation efficiency gains.RFMs were recruited within 100 km of the treatment site: ramial chipped wood (RCW), horse manure (MANR) and brewer spent grain (BSG).They were added to the land treatment unit's baseline fertilizer treatment (FERT, 'F') to measure if they improved the remediation efficiency of an engine oil-contaminated soil (7500 ± 100 mg kg -1 ).Results indicate that MANR-F was the only amendment more effective than FERT for petroleum hydrocarbons (PHC) reduction, while emitting the least CO 2 overall.RCW-F was equivalent to FERT but retained more moisture.Although BSG contributed the most nitrogen to the soil, BSG-F retained excessive moisture, emitted more VOCs, contained less soil O 2 , and was less effective than the baseline treatment.Significantly more of the C 16 -C 22 fraction was removed (63 ± 22%) than all other fractions (C 22 -C 28 , C 28 -C 34 , C 34 -C 40 ), which were equally removed.Microbial community-level physiological profiling (CLPP) was conducted with Biolog Ecoplates TM , and catabolic diversity differed between treatments (utilization rates of 31 carbon sources).MANR-F has the potential to increase PHC-remediation speed and efficiency compared to inorganic fertilizer alone.Other RFM promote moisture retention and diverse microbial catabolic activity.A variety of RFM are present across the globe and some can offer low-cost amendments to boost remediation efficiency, while reducing treatment time compared to traditional fertilizer-only methods.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.216 | 0.144 |
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".