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Record W7107873604 · doi:10.20383/103.01518

Avalanche observation datasets, Glacier National Park, British Columbia, Canada.

2025· dataset· W7107873604 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFederated Research Data Repository · 2025
Typedataset
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierNational parkSnow coverColumn (typography)

Abstract

fetched live from OpenAlex

This dataset contains recordings and observation taken from the Glacier National Park avalanche forecasting operation. The dataset provides an extensive dataset on avalanche occurrence with information, such as, size and release type. This dataset is maintained by Parks Canada staff. Thus, no major changes were made to this dataset before publication, except uniformization of certain column and data type. It is also note worthy to understand that avalanche control is regularly done within this operation. Thus, the dataset is impacted by the control and stabilization of avalanche paths. For more details, please refer to the README file. The name of each file provide insight on the date of the first and last observation of each file.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.030
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.009
Science and technology studies0.0160.002
Scholarly communication0.0420.005
Open science0.0180.019
Research integrity0.0030.013
Insufficient payload (model declined to judge)0.0030.001

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 head0.109
GPT teacher head0.364
Teacher spread0.255 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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