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Record W4406881213 · doi:10.5194/essd-17-259-2025

BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada

2025· article· en· W4406881213 on OpenAlexaffabout
Daniel Kovacek, Steven Weijs

Bibliographic record

VenueEarth system science data · 2025
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSample (material)Structural basinGeologyEnvironmental sciencePaleontology

Abstract

fetched live from OpenAlex

Abstract. The British Columbia Ungauged Basin (BCUB) dataset is an open-source, extensible dataset of attributes describing terrain, soil, land cover, and climate indices of over 1.2 million ungauged catchments in British Columbia, Canada, including trans-boundary regions. The attributes included in the dataset are similar to those found in the large-sample hydrology literature for their association with hydrological processes. The BCUB database is intended to support water resources research and practice, namely monitoring network analysis studies, or hydrological modelling where basin characterization is used for model calibration. The dataset and the complete workflow to collect and process input data, to derive stream networks, and to delineate sub-basins and extract attributes, are available under a Creative Commons BY 4.0 license. The DOI link for the BCUB dataset is https://doi.org/10.5683/SP3/JNKZVT (Kovacek and Weijs, 2023).

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes2
Has abstractyes

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