Call for Papers Recent Advances in Metamaterials and Metsurfaces
Bibliographic record
Abstract
■ New theoretical results on unusual wave interactions with meta materials and metasurfaces ■ Homogenization of metamaterials and metasurfaces (anisotropic, bianisotropic, chiral, nonlocal, GSTCs) ■ Near-zero and extreme effective parameters ■ Nonreciprocity in metamaterials ■ Nonlinear, tunable, active, and non-Foster metamaterials ■ Mm-wave, THz, and infrared metamaterials and their applications ■ Plasmonics and optical metamaterials ■ Topological metamaterials ■ Space-time meta materials and metasurfaces ■ Parity-time symmetric metamaterials ■ Quantum metamaterials ■ Analytical and numerical modeling of meta materials ■ Machine learning and inverse design techniques for on-demand metamaterial synthesis ■ Electromagnetic bandgap structures and photonic crystals ■ Micro-and nanofabrication challenges ■ Experimental validations and methods ■ Conformal metamaterials and metasurfaces ■ Commercial deployments of metamaterials ■ Device and antenna miniaturization using meta materials ■ Metamaterial-inspired devices ■ Scattering, diffraction, and absorption control with metasurfaces ■ Metasurface-based antennas ■ Novel waveguiding phenomena and devices enabled by meta materials ■ Novel sensors based on metamaterials ■ Biomedical applications of meta materials
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.231 | 0.105 |
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".