Gitanyow Archaeology, Cranberry Junction - 2020 - Airborne Coastal Observatory
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
ACO Project# 20-3016-00 Andrew Martindale and Susan Marsden have requested 7 areas of interest (AOI) in Northern BC to derive high-resolution terrain models of key archaeological sites. The focus for this project is to provide detailed bare earth models to help identify archaeology sites and allow the researchers to map traditional land use areas. The project was successfully flown on July 30th, 2020. Weather throughout BC in 2020 pushed many acquisitions back, and thus we were forced to acquire this data in summer rather than the spring. The project took place over 7 areas of interest with a total size of 50.13 km2. These sites are located along highway 37, distributed around Cranberry Junction on the Nass River, British Columbia. 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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.315 |
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