Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
Bibliographic record
Abstract
This study focuses on evaluating the positioning accuracy of smartphones in a deciduous forest environment compared to various levels of Global Navigation Satellite System (GNSS) devices. In a mixed coniferous forest with 90% broad-leaved forest (deciduous season), the accuracy of 57 test points was evaluated according to different openness levels under the forest. Taking the coordinates obtained by survey-grade GNSS devices in RTK (Real-time Kinematic) mode as standard, the accuracy of the single-point positioning (SPP) mode and precise-point positioning (PPP) mode obtained by three smartphones (one single frequency and two dual frequency), one survey-grade receiver and one recreational-grade receiver are compared. It can be found that there was a significant positive correlation between canopy openness and carrier-to-noise density(C/N0) (p < 0.05). Meanwhile, the C/N0 of survey-grade devices is significantly higher than that of smartphones. The results show that the positioning accuracy of dual-frequency smartphones under forests is better than that of single-frequency smartphones. Furthermore, the positioning accuracy of the smartphone corrected by PPP mode is better than that of the recreational-grade GNSS receiver and can achieve an accuracy of about 2.5 m in the horizontal direction, which can be used for forestry stakeout, reset and determination of forest area boundaries in environments with high openness (R > 0.7). However, in an environment with low openness (R < 0.7) and relatively complex forest area positioning, survey-grade GNSS devices are still required to cooperate with the PPP or real-time differential positioning method to obtain accurate sub-meter-level positioning data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".