Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oil sands process-affected water (OSPW), produced by surface-mining of oil sands in Canada, is alkaline and contains high concentrations of salts, metals, naphthenic acids, and polycyclic aromatic compounds (PAHs). Residual hydrocarbon biodegradation occurs naturally, but little is known about the hydrocarbon-degrading microbial communities present in OSPW. In this study, aerobic oxidation of benzene and naphthalene in the surface layer of an oil sands tailings pond were measured. The potential oxidation rates were 4.3 μmol L<sup>-1</sup> OSPW d<sup>-1</sup> for benzene and 21.4 μmol L<sup>-1</sup> OSPW d<sup>-1</sup> for naphthalene. To identify benzene and naphthalene-degrading microbial communities, metagenomics was combined with stable isotope probing (SIP), high-throughput sequencing of 16S rRNA gene amplicons, and isolation of microbial strains. SIP using <sup>13</sup>C-benzene and <sup>13</sup>C-naphthalene detected strains of the genera <i>Methyloversatilis</i> and <i>Zavarzinia</i> as the main benzene degraders, while strains belonging to the family <i>Chromatiaceae</i> and the genus <i>Thauera</i> were the main naphthalene degraders. Metagenomic analysis revealed a diversity of genes encoding oxygenases active against aromatic compounds. Although these genes apparently belonged to many phylogenetically diverse taxa, only a few of these taxa were predominant in the SIP experiments. This suggested that many members of the community are adapted to consuming other aromatic compounds, or are active only under specific conditions. 16S rRNA gene sequence datasets have been submitted to the Sequence Read Archive (SRA) under accession number SRP109130. The Gold Study and Project submission ID number in Joint Genome Institute IMG/M for the metagenome is Gs0047444 and Gp0055765.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it