Application of autonomous sensor technology to estimate selenium exposure and a site-specific selenium threshold in a Canadian boreal lake
Bibliographic record
Abstract
Abstract There is an increasing trend in the use of real-time sensor technology to remotely monitor aquatic ecosystems. Commercially available probes, however, are currently not able to measure aqueous selenium (Se) concentrations. Because of the well-described bioaccumulation potential and associated toxicity of Se in oviparous vertebrates, it is crucial to monitor Se concentrations at sites receiving continuous effluent Se input. This study aimed to estimate Se concentrations in a boreal lake (McClean Lake) downstream from a Saskatchewan uranium mill using real-time electrical conductivity (EC) data measured by autonomous sensors. Additionally, this study aimed to derive a site-specific total aqueous Se (TSe) threshold based on Se concentrations in periphyton and benthic macroinvertebrates sampled from the same lake. To characterize effluent distribution within the lake, eight Smart Water (Libelium) sensor units were programmed to report EC and temperature for five and seven consecutive weeks in 2018 and 2019, respectively. In parallel, periphyton and benthic macroinvertebrates were sampled with Hester-Dendy's artificial substrate samplers (n = 4) at the same sites and subsequently analyzed for Se concentrations. Electrical conductivity was measured with a handheld field meter for sensor data validation and adjusted to the median lake water temperature (13 °C) registered for the deployment periods. Results demonstrated good accuracy of sensor readings relative to handheld field meter readings and the successful use of real-time EC in estimating TSe exposure (r = 0.87; r2 = 0.84). Linear regression equations derived for Se in detritivores versus Se in periphyton and Se in periphyton versus sensor-estimated TSe were used to estimate a site-specific TSe threshold of 0.7 µg/L (±0.2). Moreover, mean Se concentrations in periphyton (16.7 ± 4.4 µg/g dry weight [d.w.]) and benthic detritivores (6.0 ± 0.4 µg/g d.w.) from one of the exposure sites helped identify an area with potential for high Se bioaccumulation and toxicity in aquatic organisms in McClean Lake. Integr Environ Assess Manag 2023;19:395–411. © 2022 SETAC
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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 | 0.000 | 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 | 0.001 |
| 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".