Passive Sampler for Dissolved Organic Matter in Freshwater Environments
Bibliographic record
Abstract
A passive sampler for the isolation of dissolved organic matter (DOM) from freshwater environments is described. The sampler consists of a molecular weight selective membrane (1000 kDa) and an anion exchange resin (diethylaminoethylcellulose (DEAE-cellulose)). NMR indicates the samplers isolate DOM that is nearly indistinguishable from that isolated using the batch DEAE-cellulose procedure. In a comparative study DOM isolated from Lake Ontario cost approximately 0.30 dollars/mg to isolate using the passive samplers while DOM isolated using the traditional batch procedure cost approximately 8-10 dollars/mg. The samplers have been shown to be effective in a range of freshwater environments including a large inland lake (Lake Ontario), fast flowing tributary, and wetland. Large amounts (gram quantities of DOM) can be easily isolated by increasing the size or number of samplers deployed. Samplers are easy to construct, negate the need for pressure filtering, and also permit a range of temporal and spatial experiments that would be very difficult or impossible to perform using conventional approaches. For example, DOM can be monitored on a regular basis at numerous different locations, or samplers could be set at different depths in large lakes. Furthermore, they could potentially be deployed into hard to reach environments such as wells, groundwater aquifers, etc., and as they are easy to use, they can be mailed to colleagues or included with expeditions going to difficult to reach places such as the Arctic and Antarctic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".