Ancient Forest Wetlands, BC - Upper Fraser River - 2019 - Airborne Coastal Observatory
Bibliographic record
Abstract
Ancient Forest Wetlands Survey conducted on Aug 29th 2019. Contained within this report are details regarding the project specifications, overall data accuracy, and deliverables. A survey acquisition of LiDAR and orthoimagery was carried out over Ancient Forest/Chun T'Oh Whudujut Provincial Park to generate a high-res DEM of several wetland complexes and their surrounding topography. The DEM is being used to support a project mapping the hydraulic gradient throughout the year in the wetlands in an effort to determine how significant snowmelt recharge is for these ecosystems. Hakai Institute’s Airborne Coastal Observatory (Hakai ACO) conducted an aerial survey of 14.95km2 approximately 93km south-east of the Prince George Airport over the Ancient Forest / Chun T’oh Whudujut Provincial park. The survey commenced on Aug 29th 2019 and finished on the same date. Data collected includes: LiDAR, 4 band digital imagery, and Inertial Navigation System (INS) data Using combined GNSS and Inertial Movement Unit (IMU) data, the Hakai geospatial group calculated aircraft attitude and position, and incorporated laser range data to resolve spatial surface information. The flight consisted of 8 parallel passes over the Ancient Forest/ Chun T’oh Whudujut Provincial Park and the INS positional data was processed immediately after the completion of the project. Previous calibration flights allowed Hakai’s geospatial group to calibrate the roll, pitch, and heading of the system and refine the acquired spatial data. Aircraft position data was processed using an Applanix CenterPoint RTX solution by combining multiple continuous operating GNSS base stations to generate a set of observations as base stations to post-process Differential GNSS processing. The aerial survey combined with previously flown calibration flights aided in resolving and validating vertical elevations and geo-referenced aerial photos. Hakai Institute’s geospatial team completed all data acquisition, data post-processing and quality analysis. Geodetic Parameters Horizontal Datum: NAD83 (CSRS) Epoch: 2010.00 Vertical Datum: CGVD2013 Geoid: CGG2013a Projection: UTM Zone 10 N Units: Metres Point Specification Aggregated Nominal Point Density (ANPD): 7.3 pts/m2 Aggregated Nominal Ground Point Density (ANGPD): 2.71pts/m2 Orthophoto Resolution: 7.4 cm pixel resolution Data products available: Lidar data (LAZ)- classified point cloud – digital surface model – digital terrain model. Image data (TIFF) – 4 band orthophotos – RGB & NIR. Hyperspectral data (not always captured). A detailed project report with the summary of acquisition, processing, and overall hardware / software is available (PDF). Sensors and instrument breakdown: Inertial Navigation System: Manufacturer: Applanix (Canada), IMU Model: POS AV 510 IMAR, GNSS Model: Trimble AV39. Laser sensor: Riegl LMS-Q 780 long-range airborne laser scanner. Point density ranges per project and landscape from 1-12 points per square meter. Aerial cameras: two fully integrated Phaseone Industrial iXU-RS1000 medium format cameras, resolution: 100MP, lens: 50mm f/4.0 Rodenstock. Hyperspectral Sensor: manufacturer: Specim, model: AisaFENIX 384, spectral range: 380 - 2500 nm
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 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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.218 |
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; both teacher heads agree on what is shown here.
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".