Porosity Measurement of Low Permeable Materials Using Gas Expansion Induced Water Intrusion Porosimetry (GEIWIP)
Bibliographic record
Abstract
Porosity measurement is a key factor to identify the hydraulic performance of low permeable porous materials (e.g. rock or concrete). Porosimetry tests such as Mercury Intrusion Porosimetry (MIP), Nuclear Magnetic Resonance (NMR), or Gas Expansion (GE) are cost-prohibitive, use hazardous materials, or are incapable of accessing all inter-connected pores. An alternative Gas Expansion Induced Water Intrusion Porosimetry (GEIWIP) method was developed to measure the porosity of a low permeable porous medium using a gas/water intrusion apparatus. This method overcomes the previously mentioned porosimetry drawbacks by using distilled de-aired water (DDW) as a hazard-free liquid which is a wetting fluid to intrude the porous structure and fill the pores. As the DDW has the tendency to fill all inter-connected pores, no back-up pressure is required. This method has lower cost and needs less preparation time comparing to MIP test. Additionally, the GEIWIP set-up, the gas/water intrusion apparatus, can be moved to the field site and provide mobile measurement feasibility. The reliability of the test results was obtained by a repetitive testing process. The porosity of concrete samples with different mixtures was obtained and compared to those of MIP and NMR tests.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".