Bibliographic record
Abstract
Vast amounts of data are generated by sensors that are used to monitor people, animals, plants, machines, structures, and the environment. Increasingly, this data is used to create relevant context based on sophisticated pattern recognition algorithms trained using past labeled data. However, most of these sensor systems are severely constrained regarding their communication and computation capabilities due to limitations on available energy, size, or location. New computational approaches are needed to overcome the limitations of existing digital processors in contextual processing. This article discusses the development of the first such computer that is entirely made based on common 3D‐printing materials and techniques. It is demonstrated that a simple structure printed with regular 3D printers can be driven and used with common measurement tools to perform sophisticated contextual computations, including standard benchmarks and a demonstration of user activity detection from sensor data. The correlation between memory capacity, nonlinearity, and sampling rates with this computer is examined. The 3D‐printed structure may be used as a stand‐alone computer to detect patterns in general data streams. Moreover, the computer can be integrated with the sensorized 3D‐printed structures, leading to the development of cognizant 3D‐printed systems comprising sensors and contextual processors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".