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Record W4393893128 · doi:10.5281/zenodo.1209371

Calibrated Radarsat-2 Soil Moisture Retrievals At The Trail Valley Creek Study Site

2018· dataset· en· W4393893128 on OpenAlexaff
Simon Zwieback, Sebastian Westermann, Moritz Langer, Julia Boike, Philip Marsh, Aaron Berg

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typedataset
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsWilfrid Laurier UniversityUniversity of Guelph
Fundersnot available
KeywordsEnvironmental scienceWater contentHydrology (agriculture)MoistureClimatologyPhysical geographyRemote sensingGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Calibrated soil moisture retrievals from Radarsat-2 along with in-situ soil moisture/soil temperature observations over 14 sites (in two different plots) in the Trail Valley Creek study area. The data are compiled in two records: i) satellitetime (at the time of the satellite overpass: 2014 and 2016), and ii) timeseries (dense time series: 2014 only; 2016 already published at doi:10.5683/SP/NDTRLG).

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.004

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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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