Lethal concentration (LC50) (120h) of neutral household detergent Limpol in guppy Poecilia reticulata
Bibliographic record
Abstract
Aquatic environments have been destroyed because of increase of pollutants dumped into waters. In some poor countries or in developing ones, like Brazil, detergents are one of the main responsible to impact these environments. Guppy (Poecilia reticulata) is a common fish in Central and South America, being very used in vitro experiments, since it is an easy specimen to keep in laboratories. This work aimed to determine the LC50 (120h) of neutral household detergent for guppy. We tested seven different concentrations (0, 10, 20, 30, 40, 70 and 100 mg/L), and Probit analysis showed that approximately 33.4 mg/L was the lethal dose that killed 50% of guppies in 120h, with doses below 30 mg/L did not killing any fish, while doses above 30 mg/L killed all individuals in few hours. We concluded that even small doses of detergent can be lethal to aquatic organisms, especially if the exposition time is prolonged.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".