Measurement of the surface water reaeration coefficient using Krypton-84 as a gas tracer
Bibliographic record
Abstract
Details are presented of a system devised to use Krypton (Kr) to quantify the reaeration coefficient in surface waters.The technique is based upon similarities in the gas transport mechanisms of dissolved Kr and oxygen (02) in water, and relies on the low natural abundance of Kr in the atmosphere and consequently in surface waters.The experimental protocols used in laboratory tests and preliminary field trials are given.Kr was released into the water bodies, either by introduction of "kryptonated water (in the laboratory) or by using a fine-pore diffuser (in field tests).In each case, a quantity of the fluorescent dye tracer, Rhodamine WT, was released simultaneously -the latter to identify the progress of the kryptonated water and to act as a conservative tracer (allowing the effects of dispersion and tracer dilution to be quantified and isolated from the gas loss through mass transfer process).Breakthrough curves (BTCs) for the tracers were monitored at predetermined locations downstream of the release point by taking a series of "grab" samples.The samples were taken and stored using predetermined protocols to ensure reliable results.The samples were analysed for Kr in the laboratory by gas chromatographmass spectrometry (GC-MS) using headspace analysis, and then for Rhodamine WT by sensitive fluorimetry.Using the BTCs for Kr and Rhodamine WT, the rate at which Kr was released from the water to the atmosphere was calculated from the mass difference between downstream points (areas under BTC) after correction for dispersionidilution effects.The rate of Kr release was then used to determine a value for the reaeration coefficient.The results of laboratory and river tests show that, with further refinement, the method could provide a viable means for evaluating the reaeration coefficient, and hence the potential for reoxygenation, of surface waters.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".