Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.
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.
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 | 0.001 | 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) | 0.000 | 0.001 |
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 it