Distribution of Water Quality in the Sasar River Basin According to the EPT (Ephemeroptera, Plecoptera, Trichoptera) Zoobenthos Bioindicators
Bibliographic record
Abstract
The hydrographic basin of the Sasar River is located in the north-western part of Romania, a highly industrialized region until 2000, its main point being the city of Baia Mare. Here the main pollutant economic fields were mining and metallurgy. Thus, pollution affected all life environments of this region, including the health of the population. This paper presents the assessment of the ecologic status of the Sasar River Basin water quality. Thus, the zoobenthic communities of the Sasar River and its main tributaries were monitored for 4 years, twice a year, during 2004-2008. The analysis of the quality zoobenthic indices offers a total view about the ecologic status of a watercourse. From the multitude of bioindicator groups, the taxons belonging to the EPT (EPHEMEROPTERA, PLECOPTERA, TRICHOPTERA) communities were chosen, due to their implied ecologic relevance. The maximum number of the taxons belonging to the EPT communities were recorded for the Valea Măriuţii station (28), followed by the Valea Limpedea station (24), and the minimum EPT taxons were recorded for the stations of Valea Firiza (1), upstream from Baia Mare (1) and downstream from Baia Mare (1).The distribution of the saprobity degree of the analyzed EPT bioindicators is as it follows: 32 oligo-saprobic individuals (26,44%), 18 oligo-beta-mezosaprobic individuals (14,87%), 39 beta-mezosaprobic individuals (32,23%) and 2 beta-alfa-mezosaprobic individuals (1,65%).
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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) | 0.001 | 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".