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Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska - 2010

2019· dataset· en· W6976977856 on OpenAlexaboutno aff

Bibliographic record

VenueEnvironmental Data Initiative · 2019
Typedataset
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRidgeArcticEddy covarianceFlux (metallurgy)Atmosphere (unit)The arcticPrecipitation

Abstract

fetched live from OpenAlex

In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnavait Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.In support of these objectives, a 3m eddy covariance station was established on a low ridge adjacent to Imnavait Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micrometeorological variables.

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)0.001
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.012

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 head0.116
GPT teacher head0.281
Teacher spread0.164 · 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
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
Published2019
Admission routes1
Has abstractyes

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Same venueEnvironmental Data InitiativeSame topicHistory of Computing TechnologiesFrench-language works237,207