Carbon-derived Substrate Accelerated the Biodegradation of Low-density Polyethylene
Bibliographic record
Abstract
Plastic is a commonly used carbon-based product that poses a major environmental pollution hazard due to its poor biodegradability.The natural environment is rich in organic carbon sources.Only few microorganisms have developed abilities to disintegrate heavy plastic molecules, and their lytic capabilities are generally poor.Microorganisms select carbon molecules that require less energy to digest.As bacteria are highly adaptable organisms, it is hypothesized that bacterial plastic biodegradation activity could be stimulated by limiting carbon sources to plastic and forcing bacteria to upregulate necessary metabolic pathways.Bacteria with natural abilities to decompose plastic from three different ecosystems in Alberta, Canada (forest, river, and farm) were cultured in an artificially created environment with limited carbon access.Pseudomonas sp.demonstrated the ability to accelerate LDPE biodegradation from 1.39% in carbon-saturated to 21.335% in carbon-restricted mediums in three months.The results of our study suggest that plastic pollution could be reduced by increasing its biodegradation in dedicated composters.Finding a solution to the overwhelming waste issue is critical to the planet's health.
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 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) | 0.000 | 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".