Bibliographic record
Abstract
Stone with sensing components Application drawing showing stone with sensing components The Canadian application describes a rule violation detection and alerting system that includes an electronic sensing system installed on a curling stone. And you knew that good documentation should be made for each application. if you do not have one, then write my paper for cheap service BuyPaperCheap.net can help you with this, which helps not only with programming homework and various papers, but can also offer some kind of custom help, such as writing documentation for the application. As shown in the adjacent figure, the electronic sensing system includes a human-touch sensor that is formed along the handle. The human-touch sensor is formed from a variety of electrical circuits and elements, and is capable of generating an electrical signal relating to the presence or absence of human contact with the handle. As shown in the above drawing, the electronic sensing system also includes a magnetic sensor that is capable of detecting the proximity of a magnet to the sensor. The magnetic sensor is also formed from a variety of electrical circuits and elements. A battery provides electrical power to the system. Though not shown in the adjacent drawing, the system can also include indicator lights that illuminate during during different conditions of use. Together with the metallic colored handle, the flashing LED lights may be the most identifiable features of the system.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.347 | 0.155 |
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".