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Record W6930381207 · doi:10.5281/zenodo.12196313

HLWATER V1.0 water bodies - Western Nunavik (Subarctic Canada)

2024· dataset· en· W6930381207 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWater bodySubarctic climateLatitudeGeographic information systemSatellite imageryHigh resolutionShapefile

Abstract

fetched live from OpenAlex

This dataset consists of a Very High Resolution water body delineation dataset computed with the HLWATER V1.0 model (Freitas et al., 2024) over PlanetScope-Dove imagery (3-m spatial resolution) for Western Nunavik (Eastern Hudson Bay), Subarctic Canada. It covers a total area of 41,832 km2 within the latitudes 54° to 58° N and the longitudes 74° to 78° W. The dataset is composed of 335,281 water bodies. Additionally, 1 km2 hexagonal grids are provided with the calculation of the limnicity (water fraction of land surface) and limnodensity (density of water bodies considering their centroids). Outputs are provided in shapefile and geodatabase formats. The manuscript detailing these outputs has been submitted to GIScience and Remote Sensing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0300.037

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 head0.023
GPT teacher head0.211
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes2
Has abstractyes

Explore more

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