Limitations of methods for preserving ammonium in agricultural runoff samples
Bibliographic record
Abstract
Ammonium concentrations in simulated and actual field runoff samples were not stable within 1 d under refrigeration due to microbial activity as shown by evolution of CO2. Changes in frozen samples after freezing and thawing appeared to be due to non-biological processes and this type of preservation should be avoided. A few drops of toluene added to 10 mL of simulated runoff sample stabilized ammonium concentrations at room temperature for 4 d and reduced CO2 evolution during that time substantially. Chemical preservation by adding sufficient HCl, LiCl and CuSO4 to have molarities of these chemicals up to 0.1 M strength was not acceptable because of limited reduction in microbial activity and chemical changes to the sample. Preservation effects of prestorage filtration were not consistent and this would not be a suitable procedure if total N measurements are to be included because a portion of the sample would be removed. It is apparent that microbial activity in runoff or similar types of water samples that contain organic C must be controlled immediately after collection if measurements of specific N ion species are to be made. Although refrigeration slows microbial activity, it may not be an adequate method of preservation for more than 1 d of storage. Even 1 d of refrigeration will be difficult to do under field conditions. Addition of a small amount of toluene to the runoff sample was the most promising preservation method examined. Key words: Runoff samples, sample preservation, ammonium
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".