Errors with Small Volume Elastic Seepage Meter Bags
Bibliographic record
Abstract
The use of small volume elastic collection bags (condoms) has become popular in seepage meter studies in recent years, despite minimal field or laboratory validation of their use and, specifically, the impact of their elasticity on seepage measurements. A laboratory study was initiated after field results using small elastic collection bags produced seepage data that did not correlate with hydrometric data. The laboratory data demonstrate that condoms undergo significant mechanical relaxation during seepage measurement times typically observed in field settings. Unlike conventional nonelastic collection bags, which mechanically relax over several minutes, the condoms suffered from a slow mechanical relaxation or equilibration. Over nine hours, condoms gained 43 mL of water, approximately 50% of maximum workable volume (between mechanical relaxation effect and elastic limit), under stagnant flow conditions. This long-term equilibration invalidates simple subtraction of equilibration volumes from collection volumes as a correction technique. Previously published studies using flexible small-volume elastic measurement bags (condoms) have not reported a mechanical relaxation effect. Overall, because the condom's small workable volume and inherent variability, we would not recommend any small-volume elastic measurement bags for quantitative seepage measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".