IWI Red River Basin Mapping 2008-2010 - LiDAR
Bibliographic record
Abstract
this lidar data is the result of private and government entities working together under the guidance of the international water institute iwi regarding the red river basin mapping initiative rrbmi the usace st louis district was one of the major contributors in this funding effort fugro horizons inc in cooperation with north west geomatics and fugro earthdata acquired lidar with leica sensor als50 ii mpia for the red river basin the red river basin covers nd mn sd and flows into canada the us red river basin boundary covers 40 860 sq with add on counties is approximately 47 100 sq miles acquisition was planned between spring 2008 and spring 2010 lidar sensor settings included acquisition at 8 000 amt 45 degree field of view 6 626ft swath width maximum along track spacing occurs at fov edge of 2 56m in overlap areas maximum cross track spacing occurs at nadir 2 16 meter average post spacing of 1 35m point density average of 0 55 points per square meter and area point average of 1 82 m 2 this sensor was also equipped with ipas inertial measuring unit imu and a dual frequency airborne gps receiver color film was acquired at 17 500 amt and its accuracy can support 0 5m ortho photography final deliverables for the lidar are in utm zone 14 coordinate system nad83 cors96 navd88 geod03 meters tiled 2 000m x 2 000m with an xy naming convention deliverables bareearth contains bare earth class 2 keypoints class 8 and red river class 9 las files have a leading b identifier first_returns contains lidar first return las files with a leading f identifier flight logs and jpgs grids_ascii 1m arc grid created from key points bare earth lidar with a leading a identifier grids_integer 1m arc grid z in centimeters created from grids_ascii with a leading g identifier hybrid hybrid 1m pixel b w tiffs with georeferencing created from lidar intensity and raw lidar hillshade leading h identifier metadata contains metadata file s processing_report includes rmse s and acquisition details raw raw lidar corrected to ground las files with a leading r identifier intensity data and return will be included 2 ground 6 building 8 model keypoint 9 water conditional on breaklines 12 overlap film at report survey report blocks abcd detail surveyor field notes and pictures of controlpoints
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.025 |
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".