The Potential for Remote Sensing Measurement of Dissolved Organic Carbon as a Tool For Metal Risk Assessments
Bibliographic record
Abstract
The biotic ligand model (BLM) is a tool used to quantitatively evaluate how receiving water chemistry affects the bioavailability of metals. Sensitivity testing can be used to understand how the model outputs vary in response to systematic changes in water chemistry inputs. This will allow users of such models to understand how accurate their input parameters must be for a specified level of confidence in the output. Our focus was on dissolved organic carbon (DOC), which is often the most limiting data for application of BLM approaches to metals risk management. To potentially address DOC data limitations remote sensing can be explored as a tool to measure DOC, but it is necessary to understand DOC data requirements to produce water quality criteria outputs within the usually accepted prediction variance. This study begins with inputting average water chemistries to a copper BLM model for both cold and warm water regions with 1%, 10%, 25% and 50% variations in the mean values for all parameters, without considerations of correlations among parameters. The variation in the model output criterion continuous concentration (CCC) for copper as a function of DOC, and other model inputs, allows estimation of how well DOC needs to be estimated using remote sensing in a theoretical sense. It was discovered that 20% error allowance in the theoretical simulations gave accurate BLM results. To address if the 20% error holds up in practice, similar testing was completed with a real data set from 100 lakes in Ontario. Interestingly, it was discovered that DOC did not have as much influence on the CCC, and pH was the most sensitive parameter. This launched an investigation as to if the 20% error allowance would differ based on different pH levels, as results showed higher pH values having higher CCC values. The modelling then showed that although CCC is dependent on pH, the error allowance is independent of pH. Therefore, the 20% error allowance of DOC measurements through remote sensing is still a reasonable guideline, regardless of pH value.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".