Freeze concentration of ambient waters for toxicity testing
Bibliographic record
Abstract
We have developed a method to concentrate aqueous samples for toxicity testing. This method relies on the phenomenon of freezing exclusion, whereby solutes are rejected from the interstices of a growing ice crystal. Tenfold freeze concentration gave excellent recoveries of inorganic and organic analytes, phenol and ZnSO4 toxicity from spiked natural waters, and toxicity of both pre- and postdischarge municipal wastewater. Simultaneous 10-fold concentration of strong mineral or humic ambient matrices did not substantially modify the expressed toxicity of phenol or ZnSO4, and it did not seem to generate spurious toxicity to the marine bioassay organism used (Vibrio fischeri). Hundredfold freeze concentration permitted the quantification of low levels of ambient toxicity in a wide variety of natural waters using a rapid, inexpensive microbioassay. Precipitation of matrix elements may limit the degree of concentration that can be achieved with highly mineralized or strongly humic waters. This approach is well suited to ambient toxicity testing, because it is nonspecific and has low potential for solvent contamination. Furthermore, the low temperatures involved minimize volatilization and degradation of organic contaminants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".