Bibliographic record
Abstract
Precise positioning techniques have undergone a remarkable evolution, transforming from traditional surveying methods to modern real-time, centimeter-level accuracy solutions enabled by Global Navigation Satellite Systems (GNSS). This chapter explores the historical advancements, key methodologies, and future trends in precise positioning, with a focus on their impact across scientific, academics, industrial, and societal domains. The discussion begins with the transition from classical geodetic techniques—such as triangulation and trilateration—to the advent of satellite-based navigation. Early GNSS applications, particularly in the 1980s and 1990s, relied on Differential GNSS (DGNSS) and code-based positioning, offering meter-level accuracy. The introduction of carrier-phase techniques, such as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), marked a paradigm shift by enabling high-precision solutions without the need for local reference stations. The chapter delves into the advancements in PPP with Ambiguity Resolution (PPP-AR), hybrid RTK-PPP methods, and the integration of multi-GNSS constellations, which have significantly improved accuracy, reliability, and global coverage. The impact of atmospheric modeling, real-time corrections, and network-based augmentation systems (e.g., SBAS, GBAS, and NRTK) is also discussed, highlighting their role in reducing positioning errors. Finally, emerging trends such as GNSS fusion with inertial sensors (GNSS/INS), AI-driven positioning, and quantum-enhanced navigation are explored, showcasing the future of precise positioning.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".