RESEARCH PROGRESS OF FIELD WORKING SYSTEM OF COMPUTER-AIDED REGIONAL GEOLOGICAL SURVEY
Bibliographic record
Abstract
Computeraided regional geological survey system is a high and new technology system which was developed rapidly in China in recent years. In the whole system field working system is the key research part. In this paper, development status and characteristic of related field working system of different countries were discussed, and their working models were also compared. The field working system of Australia's FieldPad, Canada's FieldLog, United States' GeoMapper, Switzerland's FieldBook and China's GeoSurvey were introduced. Besides the software, the hardwares used in field system are also important because of field hard working environment. Some new hardwares were also described in this paper, such as Laser Binoculars with Compass and Under Sunlight Readable Color Screen, et al. Based on the review, the system structure and research approach was pointed out. Methods of multiS integration and information system is essential to the system development. According to great progress in computer technology, new trend is the field working system based on lightsmall laptop computers. Lots of system application practice show that digital mapping system will improve evidently the efficiency and precision of traditional regional geological survey works. This computeraided system is very important to the Chinese new round national land and resources survey and Digital Territory engineering.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".