Exploring the detection of microcystin-LR using polar organic chemical integrative samplers (POCIS)
Bibliographic record
Abstract
Polar organic chemical integrative samplers (POCIS) were used in this study to explore passive sampling for the detection of microcystin-LR (MC-LR). POCIS were deployed in triplicate for a minimum of 28 days at the inlet and outlet of a 1 km2, 4.2 km long lake in Atlantic Canada. POCIS results were compared to lake water grab sampling and followed a similar trend. Laboratory POCIS studies using lake water spiked with known MC-LR concentrations were used to estimate lake water concentrations over the deployment period by calculating POCIS sampling rate (Rs). The Rs for MC-LR in this lake water was found to be 0.045 (±0.001) and 0.041 (±0.001) L per day for initial concentrations of 0.5 and 1.0 μg L-1. Estimated MC-LR concentrations from POCIS were generally higher than grab sampling results, especially at the outlet to the lake from late September to late October. This could possibly give a better picture of MC-LR concentrations in the lake; MC-LR concentrations can fluctuate substantially over short time periods. POCIS were able to detect MC-LR in the water stream when grab sampling resulted in non-detects (i.e., below detection limit of LC-MS/MS). The use of passive sampling for MC-LR could have beneficial implications for public health and toxicity testing by lowering detection limits for this chronically and acutely toxic chemical. The correlation of aqueous MC-LR concentrations to POCIS MC-LR concentrations needs further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".