Comment on “Effective thermal conductivity of metal and non-metal particulate composites with interfacial thermal resistance at high volume fraction of nano to macro-sized spheres” [J. Appl. Phys. <b>117</b>, 055104 (2015)]
Bibliographic record
Abstract
In a recent article, Faroughi and Huber [J. Appl. Phys. 117, 055104 (2015)] propose two theoretical models to compute the effective thermal conductivity of metal and dielectric spherical particle reinforced composites with interfacial thermal resistance. The models are based on the differential effective medium (DEM) theory. The authors have failed to cite and discuss the paper of Pal [Mater. Sci. Eng., A 498, 135–141 (2008)] where similar models have been derived using the same approach (DEM theory). Furthermore, the models proposed by Faroughi and Huber are seriously flawed in that the “excluded volume effect” is taken into account two times, instead of once, in their derivations. Last but not least, there are typos in their models.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".