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Record W7162659232 · doi:10.21966/g8f2-jw82

Poole Creek Aerial Surveys - Airborne Coastal Observatory

2025· dataset· W7162659232 on OpenAlexaboutno aff
Hakai Geospatial

Bibliographic record

VenueHakai Institute · 2025
Typedataset
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAerial surveyObservatoryChannel (broadcasting)Flood mythAerial photographyDebrisBathymetryLandslide

Abstract

fetched live from OpenAlex

The Hakai Institute flew a section of LiDAR across the Poole Creek area to look at the effects of the Place Creek outburst flood debris flow. This was done as part of Hakai/UNBC research on the hazard of glacier lake outburst flooding. This was completed in collaboration with Stantec Consultants, Squamish-Lillooet regional district via Graeme Vass, and the Geological Survey of Canada. The main objective was to assess channel change following the flood of 2024. The Airborne Coastal Observatory (ACO) is a collaborative program led by the Hakai Institute along with partners the University of Northern British Columbia. The ACO program offers rapid and accurate aerial observations of both terrestrial and marine ecosystems, from Icefields to Oceans, and applied across multiple scientific disciplines. Data is collected by a Piper Navajo aircraft equipped with an array of integrated Earth imaging systems and technology, including: 1) A Riegl VQ-780 airborne laser scanner; 2. Two PhaseOne iXU-RS 1000 digital medium format cameras; 3. Specim AisaFENIX Imaging Spectrometer; 4. Applanix Inertial Navigation System. All data is processed and maintained by the Hakai Geospatial Technology team. The aircraft is provided and maintained by Kisik Aerial Surveys Inc. (Delta, BC). Poole Creek study site located adjacent to the Sea to Sky Hwy, southwest of Pemberton BC.

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)0.000
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 score0.235
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.022

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 head0.037
GPT teacher head0.282
Teacher spread0.245 · 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
Published2025
Admission routes1
Has abstractyes

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