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Record W2910392088

IWI Red River Basin Mapping 2008-2010 - LiDAR

2015· dataset· en· W2910392088 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedataset
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLidarTotal stationRemote sensingGeographyDrainage basinInertial measurement unitStructural basinEnvironmental scienceCartographyGeologyEngineeringGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.926
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.025

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.

Opus teacher head0.021
GPT teacher head0.239
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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