Determination of Microbial Carbon Sources in Petroleum Contaminated Sediments Using Molecular <sup>14</sup>C Analysis
Bibliographic record
Abstract
Understanding microbial carbon sources is fundamental to elucidating the role of microbial communities in carbon cycling and in the biodegradation of organic contaminants. Because the majority of anthropogenic contaminants are either directly or indirectly derived from fossil fuels that are devoid of 14C, radiocarbon can be used as a natural inverse tracer of contaminant carbon in the contemporary environment. Here, 14C analysis of individual microbial phospholipid fatty acids (PLFA) was used to characterize the carbon sources utilized bythe active microbial community in salt marsh sediments contaminated by the Florida oil spill of 1969 in Wild Harbor, West Falmouth, MA. A specific goal was to determine whether this community is actively degrading petroleum residues that persist in these sediments. The delta14C values of microbial PLFA in all sediment horizons (contaminated and noncontaminated) matched the delta14C of the total sedimentary organic carbon after petroleum removal, indicating that no measurable metabolism of petroleum residues was occurring. This result agrees with ancillary data such as the delta13C content and distribution of PLFA, and the residual hydrocarbon composition determined by comprehensive two-dimensional gas chromatography (GCxGC) analysis. We hypothesize that microbes have chosen to respire the natural organic matter rather than the residual petroleum hydrocarbons because the former is more labile. Future efforts directed at determining indices of microbial degradation of petroleum hydrocarbons should consider competition with natural organic matter.
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 | 0.001 | 0.001 |
| 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".