Algebraic Model for Agent Explicit Knowledge in Multi-agent Systems
Bibliographic record
Abstract
Endoscopy, as we know it today, is performed using a flexible endoscope (Figure 2(a)), sometimes also referred to as videoscope.This type of endoscope has been introduced in the mid 1960s.While the first endoscopes used fiber optics and an eyepiece lens to visualize the inner cavities of the human body, modern endoscopes are very compact devices, including a light source, and a CCD or CMOS chip for taking pictures.But the basic concept did not change very much since those days.In addition to the digital imaging chip, modern endoscopes contain a light source at the distal tip and are equipped with an accessory channel, which allows the entry of medical instruments for example to take tissue samples, perform cleansing of poorly prepared areas, perform polypectomies, and perform endoscopic resections without any invasive surgery involved.More recent advances in endoscopy are zoom-endoscopy and chromoendoscopy.Zoom-endoscopy allows to zoom in at regions of interest, using a magnification factor of up to 150.Such devices offer a significant advance since smaller and finer details in the region to be examined get uncovered (Hurlstone et al., 2004).Another possibility to obtain images with a higher level of detail are high definition (HD) endoscopes, which also provide images of higher resolutions and therefore allow to detect subtle changes in the mucosa.Chromoendoscopy aims at enhancing superficial patterns on a mucosal layer by topically 196
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".