Direct and continuous measurement of dissolved carbon dioxide in freshwater aquatic systems—method and applications
Bibliographic record
Abstract
Abstract Understanding of the processes that control CO 2 concentrations in the aquatic environment has been hampered by the absence of a direct method to make continuous measurements over both short‐ and long‐term time intervals. We describe an in situ method in which a non‐dispersive infrared (NDIR) sensor is enclosed in a water impermeable, gas permeable polytetrafluoroethylene (PTFE) membrane and deployed in a freshwater environment. This allows measurements of CO 2 concentration to be made directly at a specific depth in the water column without the need for pumps or reagents. We demonstrate the potential of the method using examples from different aquatic environments characterized by a range of CO 2 concentrations (0·5–8·0 mg CO 2 ‐C l −1 , equivalent to ca 40–650 µmol CO 2 l −1 ). These comprise streams and ponds from tropical, temperate and boreal regions. Data derived from the sensor was compared with direct measurements of CO 2 concentrations using headspace analysis. Sensor performance following long‐term (>6 months) field deployment conformed to manufacturers' specifications, with no drift detected. We conclude that the sensor‐based method is a robust, accurate and responsive method, with a wide range of potential applications, particularly when combined with other in situ sensor‐based measurements of related variables. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".