Bibliographic record
Abstract
Watch various applications from META in action, including laser glare protection, de-fogging and de-icing, automotive HUDs and transparent antennas to name several. We have applied our technology to revolutionize everything from solar solutions to aircraft safety, to wearable technology. All of our products are designed and manufactured with environmental sustainability as a high priority. Our technology: Holography: Not all holograms portray objects. Holograms can have unique, often extraordinary functional properties. META designs and fabricates holograms as specialty optical elements that can not only replace traditional lenses and mirrors but can provide optical functions that are very difficult to achieve with conventional optics. These holographic optical elements (HOEs) allow system designers to develop devices that are smaller, lighter, cheaper and better than those achieved with conventional optics. Lithography: Rolling Mask Lithography® (RML) is our patented manufacturing technology that offers a unique advantage in the smart materials industry. RML employs a massively parallel patterning scheme that is easily scalable to large areas of rigid substrate materials (plates and panels) and rolls of flexible films. Its nano-fabrication method combines the advantages of Soft Lithography and Near-field Optical Lithography, proved to be reliable in fabrication of nano-structures beyond the diffraction limit. Wireless Sensing: can manipulate electromagnetic waves in ways that have not been possible until now, opening the door to a new generation of medical diagnostic tools. META is developing a range of devices that integrate metamaterials with unique properties for a range of different applications. www.metamaterial.com shop.metamaterial.com marketing@metamaterial.com
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".