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Record W2317836024 · doi:10.5194/asr-13-43-2016

Flux measurements by the NRC Twin Otter atmospheric research aircraft: 1987–2011

2016· article· en· W2317836024 on OpenAlexaffabout
R. L. Desjardins, Devon E. Worth, J. I. MacPherson, Matthew Bastian, Ramesh Srinivasan

Bibliographic record

VenueAdvances in science and research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNational Research Council CanadaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEnvironmental scienceGreenhouse gasAtmosphere (unit)MethaneRange (aeronautics)Work (physics)EcosystemMeteorologyAtmospheric sciencesEcologyGeographyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract. Over the past 30 years, the Canadian Twin Otter research group has operated an aircraft platform for the study of atmospheric greenhouse gas fluxes (carbon dioxide, ozone, nitrous oxide and methane) and energy exchange (latent and sensible heat) over a wide range of terrestrial ecosystems in North America. Some of the acquired data from these projects have now been archived at the Flight Research Laboratory and Agriculture and Agri-Food Canada. The dataset, which contains the measurements obtained in eight projects from 1987 to 2011 are now publicly available. All these projects were carried out in order to improve our understanding of the biophysical controls acting on land-surface atmosphere fluxes. Some of the projects also attempted to quantify the impacts of agroecosystems on the environment. To provide information on the data available, we briefly describe each project and some of the key findings by referring to previously published relevant work. As new flux analysis techniques are being developed, we are confident that much additional information can be extracted from this unique data set.

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)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.012
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.347
Teacher spread0.303 · 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
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

Citations17
Published2016
Admission routes2
Has abstractyes

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