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Record W2797680239 · doi:10.5194/essd-10-1655-2018

The Cariboo Alpine Mesonet: sub-hourly hydrometeorological observations of British Columbia's Cariboo Mountains and surrounding area since 2006

2018· article· en· W2797680239 on OpenAlexafffundabout
Marco A. Hernández‐Henríquez, Aseem R. Sharma, Mark Taylor, Hadleigh D. Thompson, Stephen J. Déry

Bibliographic record

VenueEarth system science data · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrometeorologySnowEnvironmental scienceTundraAutomatic weather stationSnowmeltAlpine climateClimatologyHydrology (agriculture)MeteorologyPrecipitationGeologyGeographyArctic

Abstract

fetched live from OpenAlex

Abstract. This article presents the development of a sub-hourly database of hydrometeorological conditions collected in British Columbia's (BC's) Cariboo Mountains and surrounding area extending from 2006 to present. The Cariboo Alpine Mesonet (CAMnet) forms a network of 11 active hydrometeorological stations positioned at strategic locations across mid- to high elevations of the Cariboo Mountains. This mountain region spans 44 150 km2, forming the northern extension of the Columbia Mountains. Deep fjord lakes along with old-growth western redcedar and hemlock forests reside in the lower valleys, montane forests of Engelmann spruce, lodgepole pine and subalpine fir permeate the mid-elevations, while alpine tundra, glaciers and several large ice fields cover the higher elevations. The automatic weather stations typically measure air and soil temperature, relative humidity, atmospheric pressure, wind speed and direction, rainfall and snow depth at 15 min intervals. Additional measurements at some stations include shortwave and longwave radiation, near-surface air, skin, snow, or water temperature, and soil moisture, among others. Details on deployment sites, the instrumentation used and its precision, the collection and quality control process are provided. Instructions on how to access the database at Zenodo, an online public data repository, are also furnished (https://doi.org/10.5281/zenodo.1195043). Information on some of the challenges and opportunities encountered in maintaining continuous and homogeneous time series of hydrometeorological variables and remote field sites is provided. The paper also summarizes ongoing plans to expand CAMnet to better monitor atmospheric conditions in BC's mountainous terrain, efforts to push data online in (near-)real time, availability of ancillary data and lessons learned thus far in developing this mesoscale network of hydrometeorological stations in the data-sparse Cariboo Mountains.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.050
GPT teacher head0.235
Teacher spread0.184 · 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 designObservational
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

Citations11
Published2018
Admission routes3
Has abstractyes

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