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Record W6892196221 · doi:10.5066/p9zxtw4w

Geospatial Fabric for the National Hydrologic Model Alaska Domain, version 1

2020· dataset· en· W6892196221 on OpenAlexaboutno aff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedGeological surveyGeospatial analysisVfloHydrology (agriculture)MetadataHydrological modellingGeographic information systemSurface runoff

Abstract

fetched live from OpenAlex

This metadata record documents a geospatial dataset for the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS) used to drive the National Hydrologic Model (NHM). The Alaska Geospatial Fabric v1 is the spatial representation of the hydrologic response units (HRUs) used for the PRMS NHM Alaska domain. These HRUs were generated using the twelve-digit Hydrologic Unit Code (HUC12) watershed from the U.S. Geological Survey's Watershed Boundary Dataset (USGS, 2019), the Natural Resources Canada National Hydrographic Network (NHN) Work Units (NHN, 2019), similar to USGS eight-digit HUC watersheds, and stream gage locations from the U.S. Geological Survey (USGS, 2019) and Natural Resources Canada (NHN, 2019). Watershed-to-watershed routing was added to all Canadian Work Units and updated in twelve-digit HUCs from topographic map examination to ensure connectivity from the headwaters of the domain to the ocean. Watersheds containing one or more stream gages were bisected using standard watershed delineation techniques to ensure accurate contributing area for each gage. Gages near watershed boundaries were not used to bisect the watershed. Following these processing steps, these watersheds became the HRUs used for the initial version of the National Hydrologic Model Alaska Domain. Overlapping watershed vector lines were not unified. The stream gages used for this exercise became the points of interest (POIs) for use in the Alaska Domain. Stream segments used to route water from HRUs to stream outlets were generated using the routing information in the HRUs and the centroids of the HRUs. Please refer to the lineage elements of this metadata record for the above citations. U.S. Geological Survey, 2019, USGS National Watershed Boundary Dataset, ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/ Environment Canada, 2016, Natural Resources Canada National Hydro Network (NHN) Work Units, https://www.nrcan.gc.ca/science-data/science-research/earth-sciences/geography/topographic-information/geobase-surface-water-program-ge/watershed-boundaries/20973

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 categoriesMeta-epidemiology (narrow), 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 score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.019

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 head0.027
GPT teacher head0.238
Teacher spread0.212 · 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
Published2020
Admission routes1
Has abstractyes

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