Evaluation of multi-assemblage metrics and temperate indices as indicators of human impact in Lake Ziway, Ethiopia
Bibliographic record
Abstract
Lake bioassessment is routinely done using one biological community such as macro-invertebrates, diatoms, macrophytes or fish. Theuse of at least two assemblages has been suggested as they are believed to be more robust indicators, because each community responds differently to potential stressors in waters. This study aimed to use macroinvertebrate and diatom assemblages to identify metrics and temperate indices that could discriminate between reference and impacted sites of the littoral zone of Lake Ziway, Ethiopia. The Lake Habitat Quality Assessment (LHQA) method was used to categorize the sites in the littoral zone of the lake. Lake water, macroinvertebrate, and diatom samples were collected from 3 reference and 6 impacted sites between September 2015 and April 2016 with standard methods and following the Ontario Benthos Biomonitoring Protocol. A total of 34 macroinvertebrate taxa and 39 diatom species were recorded. 32 macroinvertebrate and 18 diatom indices were tested for their ability to discriminate between the reference, intermediate and multiple-stressed sites using correlations between metrics, similarity values with SIMPER and boxplots overlaps. Further, correlation of the metrics with physico-chemical parameters extracted metrics and indices with high discrimination efficiency(≥3). The indices remaining were 5 macroinvertebrate (NT, PTI, PETI, PDT and CLI) and 4 diatom (CEE, PTV, TDI, and IBD) indicators which clearly discriminated between the impacted (intermediate and multiple stressors) and reference sites, but not within the impacted sites, in the lake. These data can also possibly serve as core metrics for multi-assemblage index development for this shallow, tropical lake. Key words/phrases: Diatom, Discriminatory efficiency, Lake bioassessment, Macroinvertebrate, Multi-assemblage, Omnidia, SIMPER
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".