Phytotoxkit: A critical look at a rapid assessment tool
Bibliographic record
Abstract
Terrestrial plant toxicity testing contributes critical information to many site risk assessments, but standardized tests can be labor-intensive, use large amounts of soil, and employ long test durations. The Phytotoxkit (MicroBioTests, Environmental Bio-Detection Products) minimizes the time and cost associated with terrestrial plant testing with a unique test setup, a shorter test duration, and less soil. However, the sensitivity of the test remains an open question. In this research, the Phytotoxkit and the standardized Environment Canada terrestrial plant toxicity test (definitive test) are compared using a parallel testing approach. Three different scenarios were examined: a multiconcentration test, in which an inhibiting concentration (ICp) was derived from chemically amended soils; a soil remediation test, in which plant growth in a remediated soil was compared to the original contaminated soil; and a site soil test, in which plant growth in a contaminated soil was compared to a reference soil. The contaminants tested were boric acid, Cr(VI) with cyclodextrin as a remediation agent, and petroleum hydrocarbons. Trifolium pratense (red clover) was used in the first and second scenarios, and six different plant species were used in the third scenario. In the first scenario, the Phytotoxkit results compared well with the definitive test results after 5 and 7 d of exposure. In the second scenario, the Phytotoxkit results agreed with the definitive test when evaluating the effectiveness of remediation. In the third scenario, the Phytotoxkit results were often not in agreement with the results from the definitive test. The reduced sensitivity of the Phytotoxkit in the third scenario may be driven by test unit design, as plant roots are separated from soil by filter paper.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".