Bibliographic record
Abstract
With the progress of society and technology and the acceleration of people's life rhythm, nowadays, location-based services play an indispensable role in people's lives. For mobile users using mobile devices such as smartphones, location-based services are an integral part of life. Location-based services usually obtain the location information (geographic coordinates, or geographic coordinates) of mobile terminal users through the wireless communication network (such as GSM network, CDMA network) or external positioning (such as GPS) of mobile telecommunication operators. The accelerated pace of life also means more and more trips and transportation. In many cases, people would like to meet friends or family at a particular place. At the same time, they hope to observe each other's location and route during the journey and even hope to be able to chat by text or voice when they go to their destination together. The purpose of this project is to develop a map application program, which allows users to share real-time location, communication, and chats with friends or family members. Besides, if the user establishes a meeting place with friends, the application can make routes for the "travel together" people respectively, and the user can see the routes of friends on the map. This project collects useful insights and design ideas, and refers to and studies related applications to help the development of products in this project. In the future, more in-depth research can also be carried out to improve based on this project.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".