Bibliographic record
Abstract
Fin Island & K'yel # 20-3015-00 The airborne survey covered three sites on the north coast, near Hartley Bay, BC. Collaborators are Bryn Letham, Dana Lepofsky, and the Gitga’at FN. These sites represent important village sites in Gitga’at Territory. Lidar and imagery will be part of an interdisciplinary research program with ecologists and marine biologists studying long-term intertidal harvesting patterns as they relate to resilience and sustainability. The archaeologists (Bryn and Dana) are interested in continuing/expanding the postglacial sea level and paleoshorline study, as well as making beautiful maps for modern beaches, shorelines, and other geomorphological features. Low tide is preferred as there are intertidal features that would be valuable to capture. The project was successfully flown on September 1st, 2020. 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.077 | 0.048 |
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".