Bibliographic record
Abstract
Two new equations are developed for effective electrical conductivity of concentrated particulate composites using a differential scheme along with the solution of an infinitely dilute dispersion of particles in a continuous matrix. The proposed equations are evaluated using 16 sets of experimental data on the electrical conductivity of two-phase particulate systems. The following model developed in the paper describes the experimental data very well: (σ/σm)1/3 (σd — σm)/ (σd — σ) = (1 — φ/φm )—αφm ) where σ, σm and σd are electrical conductivities of composite, matrix, and dispersed phase (filler) respectively, φ is volume fraction of filler, φm is the maximum packing volume fraction of filler, and α is a constant of the order of unity. In the special case of α = 1 and φm = 1, this model reduces to the well-known Bruggeman equation for the electrical conductivity of two-phase particulate systems. The predictions of the proposed model are significantly different from the predictions of the existing general effective media (GEM) model.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".