Bibliographic record
Abstract
PAGE 131 The quality control of manufactured glass containers is of paramount importance to industry due to their cost effectiveness. A collaboration of researchers from Canada and China have designed a practical inspection system based on deep learning networks to inspect the containers with greater speed and accuracy, demonstrating the continued wide-ranging applications of deep learning methods. PAGE 119 A compact smooth horn antenna with a flat-top radiation pattern has been presented by researchers from China and the UK. The antenna is made compact using a large flare angle to reduce its length with the resulting radiation pattern achieving low amplitude ripple in its intended beamwidth range. The design has applications in the mm-wave band. Samples of acquired images of glass containers. PAGE 149 Researchers in China have analysed the characteristics of streamlined ballistic targets, as these reflect real life projectiles better than current investigations with cone and blunt-nosed shaped targets. The analysis is verified using electromagnetic calculation and simulation. Image of the compact smooth horn. PAGE 144 A team of researchers from France report an Indium-Arsenide (InAs) based quantum cascade laser (QCL) operating close to 25 micrometres with thick n-doped cladding layers used for optical confinement. The device operated up to 240 K and exhibited the best performance in threshold current density for all QCLs emitting above 20 micrometres. Micro-motion model of ballistic target. PAGE 153 Planetary bodies can be investigated with orbital radar sounders with the chosen high frequency signals allowing high penetration but being subject to electromagnetic interference. The proposed model seeks to suppress this interference to improve the resolution in the received signals and presents results from the Mars Advanced Rover Sounding. Laser waveguide cross section. Data frame comparison.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.596 | 0.436 |
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".