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Record W2801517077 · doi:10.3334/ornldaac/1569

ABoVE: Hyperspectral Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019

2018· article· en· W2801517077 on OpenAlexaboutno aff
Christine E. Miller, R.O. Green, D. R. Thompson, Andrew K. Thorpe, Michael L. Eastwood, Ian B. McCubbin, Winston Olson-Duvall, M. Bernas, Charles M. Sarture, Scott H. Nolte, Luis M Rios, Hernández, Brian Bue, S. Lundeen

Bibliographic record

VenueOak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics · 2018
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsHyperspectral imagingArcticRemote sensingGeologyEnvironmental scienceGeographyOceanography

Abstract

fetched live from OpenAlex

This dataset provides Level 1 radiance and Level 2 surface reflectance measured by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument during flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain between June and August 2017, July and August 2018 and again in July and August 2019. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution. The data include 556 flight lines covering areas of interest to the ABoVE campaign over much of Alaska and western Canada. These data will allow researchers to characterize ecosystem structure and function near the height of the growing season. This dataset represents one part of a multisensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE.

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)0.000
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 score0.157
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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