Bibliographic record
Abstract
The focus of this note is to investigate the hydraulic conductivity behavior of clay mixed with nanomaterials. Two different nanomaterials — namely, gamma-aluminum oxide powder (γ-Al 2 O 3 ) and nano-copper oxide (CuO) — were selected and mixed with clay at different percentages (i.e., 2%, 4%, and 6%). Hydraulic conductivity tests were carried out in a flexible wall permeameter following the ASTM D5084 standard. Mercury intrusion porosimetry (MIP) tests were also carried out to determine the pore-size distribution. At 2% of γ-Al 2 O 3 and nano-CuO, the hydraulic conductivity of clay decreased 30% and 45%, respectively. As the proportion of the nanomaterial increases, the reduction of hydraulic conductivity becomes less prominent as flow paths devoid of nanomaterials are unlikely. Reduction of hydraulic conductivity is due to the pores of clay being clogged by the nanomaterial. Pore-size distribution curves show that the largest pore size reduced by 20% when clay was mixed with 4% nano-CuO.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".