Fatty Acid and Alcohol Distributions and Sources in Surface Sediments of Imo River, Southeast Niger Delta, Nigeria
Bibliographic record
Abstract
The distributions and concentrations of extractable fatty acids (FAs) and alcohols (FALs) in surface sediments of the Imo River were determined to estimate the relative proportion of terrigenous and autochthonous fractions of organic matter (OM) input to the river. The range of total organic carbon (TOC) content (2.10-4.58%) is typical for coastal environments and comparable to those of other river systems within the Niger Delta region. This may be a reflection of the sheltered basin morphology and high energy conditions of the river, characterized by overwhelming sand fraction. The contribution from terrestrial vegetation appears to predominate the distribution of FAs (21.17-75.16%) and alcohols (38.7-81.7%), in contrast to many aquatic sediments whose distributions are either phytoplankton or bacteria dominated. This may be linked to the relatively shallow water depth, oxic and refractory nature of the sedimentary organic carbon of the area as well as proximity of most sampling points to terrigenous source. Utilization of ratios confirmed this pattern and indicated that the OM deposited at the time of sampling was not fresh. The dominance of 19:0 acids in some samples is associated to an input from a certain consortium of bacteria with a different physiological structure inhabiting petroleum contaminated environment.
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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 | 0.000 | 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".