Characterization by benthic macroinvertebrates and some environmental factors of streams in the East Cameroon region
Bibliographic record
Abstract
Macrobenthic fauna have recently been widely used as bio-indicators for their ability to reflect the various disturbances in aquatic ecosystems. They have recently been used to assess the ecological health of streams in the East Cameroon region. This study aims to reveal the ecological health of four streams by studying the variations in the population of benthic macroinvertebrates collected in them. Sampling was done from December 2018 to December 2019 for a total of 13 months spread over four collection seasons. Kohonen's self-organising map (SOM) was performed for the various distribution patterns of the organisms collected. Discriminant factor analysis (DFA) was used to identify the parameters that characterise these patterns observed in the environment. Four groups of macrobenthic populations were observed. The distribution of benthic macroinvertebrates in these streams was spatial, temporal and discriminated by variable mineralization parameters and sediment grain size. The distribution of taxonomic richness is linked to the environmental conditions of the stations, which appear to be more or less stable, highlighting a stress gradient on the organisms. The station (Sen3), with unstable conditions, is the site of anthropic activities due to its proximity to residential areas, which are enriched in organic matter and, as a result, abound in pollutant-resistant species such as diptera (Chironomus holomelas, Chironomus sp1 and Chironomus sp2). The population of the other well differentiated groups is subject to light anthropogenic disturbance.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".