Macroinvertebrates functional feeding groups (FFGs) of forested streams draining urban catchments in the Niger Delta: identifying and classifying vulnerable and tolerant FFGs
Bibliographic record
Abstract
This study aims to assess the vulnerability and tolerance levels of macroinvertebrate functional feeding groups (FFGs) in forested streams draining urban areas in the Niger Delta. It contributes to our understanding of how human activities affect freshwater ecosystems. Between 2008 and 2012, we conducted monthly measurements of physicochemical variables and collected macroinvertebrates. We categorised sampling sites into three groups based on disturbance levels: least impacted sites (LIS), moderately impacted sites (MIS), and heavily impacted sites (HIS). Multivariate (RLQ) analysis was used to visualise associations among physicochemical variables across the sampled sites, revealing distinct relationships between certain FFGs and different sites. The RLQ model we constructed showed that grazers and collector-filterers were predominantly observed in LIS, suggesting their vulnerability to environmental stressors. Conversely, predators and shredders were more prevalent in impacted sites (MIS and HIS), indicating their tolerance to disturbances such as elevated nitrate levels and higher water temperatures. The fourth corner graph highlighted differing responses of FFGs to physicochemical variables, with predators showing positive correlations with several factors but no significant association with phosphate levels. Overall, these findings showed the importance of considering the responses of different FFGs to environmental variables in assessing the health and integrity of aquatic ecosystems.
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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".