The impacts of biofouling on automated phosphorus analysers during long-term deployment
Bibliographic record
Abstract
In-situ nutrient analysers are a promising tool for improving the temporal resolution of data and filling knowledge gaps in drivers of harmful algal blooms. There are significant challenges however regarding instrument biofouling and data drift, which remain largely unquantified and unresolved. In this study the effects of biofouling on data consistency and accuracy is quantified on automated wet chemical analysers during long-term monitoring. In 2019 three fractions of phosphorus (P); total phosphorus (TP), total dissolved phosphorus (TDP) and soluble reactive phosphorus (SRP), were measured in-situ at four sites in Southern Ontario, Canada. The analysers were exposed to a wide range of P concentrations and biofouling extremes over an 8-month period. They were calibrated using chemical standards both in the field and the lab, and validated with fortnightly grab samples, and the representativeness of real-time data under a range of biofouling conditions were analysed. Results show that analysers biofouling during long-term deployment can desensitize instrument measurements, with greatest impacts on instruments operating in highly turbid environments. Temporal changes in calibration curves suggest that equilibrium P concentrations (EPC0) of sediments accumulating inside filters can elicit a rapid exchange of dissolved P (SRP, TDP) with the water sample. Data drift increases the further from the EPC0 an instrument is required to analyse, and thus this study demonstrates that for in-situ P monitoring, unless filters are frequently replaced or renovated, in-situ probes should ideally be dedicated to a specific waterbody type defined by similar EPC0 values. It is recommended that in order to ensure accuracy in in-situ monitoring of TP, TDP and SRP during long-term deployment, preliminary site trials should be conducted to ascertain sediment EPC0; the extent of biofouling should be monitored; and/or frequent grab samples taken for post-deployment validation. The findings apply to any in-situ phosphorus monitoring techniques for SRP or TDP.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".