Geochemical Characterization of the Sources and Distribution of Fatty Acids in Recent Sediment Cores from Cross River System, South-South, Nigeria
Bibliographic record
Abstract
Background and Objective: Fatty acids have been utilized to trace organic matter source, fate and fluxes transmitted between estuaries, rivers, tributaries and their drainage basins worldwide.However, such correspondence is scarce in Sub-Saharan African region, particularly in sediment cores of Cross River system.A comparative evaluation of recent sediment cores (50 cm long) from the upper and lower Cross River system, was undertaken to characterize the sources, distributions and fate of fatty acids.Materials and Methods: These were achieved by solvent extraction, purification, identification/quantification using gas chromatography-mass spectrometry and principal component analysis-multiple linear regression statistics.Dispersion of fatty acid classes followed the trends short-chain>long-chain>branched-chain>monounsaturated fatty acids for the upper system and short-chain>branched-chain>long-chain>monounsaturated for the lower.Results: Principal component analysis-multiple linear regression apportioned 53.14% to a mixed source of phytoplankton (dinoflagellate), heterotrophic bacteria and terrestrial plant (Rhizophora avicennia) and 48.86% to a mixed source of phytoplankton (diatom) and photosynthetic bacteria in the case of the upper system.For the lower, a mixed source of phytoplankton (dinoflagellate), heterotrophic bacteria and terrestrial plant Nypa fruticans made the larger contribution of 93.32%, while photosynthetic bacteria and terrestrial plant metabolites contributed 6.68% to the total fatty acids flux.The irregular fatty acids distribution patterns observed down cores were attributed to differences in the water depth, sediment texture, biodegradation and depositional environmental conditions.Conclusion: Detection of considerable levels of bacterial fatty acids suggested the involvement of an essential aerobic/anaerobic microbial activity and operation of varying biogeochemical processes at these sites.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".