Specific polarizability of sand-clay mixtures with varying ethanol concentration
Bibliographic record
Abstract
I utilize the concept of specific polarizability (cs), represented as the ratio of mineral-fluid interface polarization per pore-normalized surface area Sp, to emphasize the influence of clay mineralogy and fluid chemistry on complex conductivity (CC) measurements. CC measurements were performed on kaolinite- and illite- sand mixtures as a function of varying ethanol (EtOH) concentration (10% and 20% v/v). Specific surface area of each clay type and Ottawa sand was determined by nitrogen gas adsorption-BET method. I also calculated porosity and saturation of each mixture based on weight loss of dried samples. Debye decomposition, a phenomenological model, was applied to the CC data to determine normalized chargeability (mn). The cs¬ estimates from previous CC measurements for bentonite-sand mixtures were compared with our dataset. The cs for all sand-clay mixtures decreased as the EtOH concentration increased from 0% to 10% to 20%. We observe similar responses to clay-driven polarization for all sand-clay mixtures. Analysis of variance (ANOVA) with a level of significance α = 0.05 found that suppression in cs responses with increasing EtOH concentration statistically vary for all sand-clay mixtures but the confidence level for cs is low. On the other hand, real conductivity showed only small changes with increasing EtOH concentration from 10% to 20%. The cs estimates reflect the sensitivity of CC measurements to alteration in surface chemistry at the available surface adsorption sites (internal and external) for different clay types assumed to result from chemical ion exchange at clay surface and kinetic reactions in the electrical double layer of the clay-water-EtOH media.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".