The establishment of a new culture of <i>Hyalella azteca</i> that would permit toxicity tests to be conducted on low–ionic strength waters
Bibliographic record
Abstract
Abstract The objective of the present study was to establish a culture of Hyalella azteca that could be used for laboratory toxicity testing in low–ionic strength waters with electrical conductivities of <200 μS/cm. A wild strain of H. azteca was collected from Twin Lake, a small seepage lake with an electrical conductivity of 81 ± 27 μS/cm located on the property of Chalk River Laboratories in Chalk River, Canada. To determine the minimum aqueous ion requirements for an optimal culturing medium for the Twin Lake strain, Twin Lake was monitored for water quality and ionic content over 4 yr. Water quality parameters were averaged and used to formulate a medium containing NaHCO3, CaCl2, MgSO4, KCl, NaBr, NaF, and LiCl, with an electrical conductivity of 89 ± 3 μS/cm. By evaluating survival and reproduction, it was concluded that this artificial medium promoted survival and supported reproduction (10 ± 4 neonates/female/wk) of the Twin Lake amphipod. The Twin Lake strain of H. azteca can, therefore, be maintained in laboratory settings, and this allows for toxicity testing to be conducted on low–ionic strength waters. Environ Toxicol Chem 2019;38:585–590. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".