Identifying the usefulness of microbial enumeration, diversity, and respiration for implementing strategies for intrinsic remediation
Bibliographic record
Abstract
Hydrocarbon-utilizing bacteria have been found widely distributed in natural environments, their proportions affected by many factors, such as level of previous hydrocarbon exposure and from variations in soil conditions. This research compares microbial enumeration, diversity, and respiration between contaminated and adjacent uncontaminated soils and three native agricultural soils from Manitoba at a variety of depths. The purpose is to understand the effects of hydrocarbon contamination on these microbial parameters and the usefulness of the parameters for implementing intrinsic remediation as a remedial option. Four contaminated soils were examined, varying in the type of previous hydrocarbon exposure (two diesel fuel, one coal tar, and one crude oil). The enumeration of aliphatic and aromatic degrading microorganisms from contaminated versus adjacent uncontaminated soils and agricultural soils were examined using a most-probable number method. The effect of previous contamination on the microbial diversity of soils was examined in a second experiment. The final experiment was to determine differences in degradation rates in contaminated versus uncontaminated soils and agricultural soils upon the addition of 14C-labelled phenanthrene. (Abstract shortened by UMI.)
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".