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Canopy height, spectral reflectance (NDVI), and aboveground biomass of Salix richardsonii across a wet graminoid-shrubland ecotone on Qikiqtaruk - Herschel Island, Yukon, Canada (2016)

2021· dataset· en· W6969084085 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Environmental Data Service · 2021
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsEcotoneBiomass (ecology)Vegetation (pathology)Ground truthCanopyMultispectral imageSatelliteReflectivityRadiometry

Abstract

fetched live from OpenAlex

This dataset consists of (i) 673 red-green-blue (RGB) images, (ii) precise coordinates of ground control points and harvest plot corners, (iii) the photogrammetrically reconstructed dense point cloud (comprising of 228,315,000 points with XYZ and RGB values), (iv) four normalised difference vegetation index (NDVI) maps, (v) aboveground biomass data from harvest plots, and (vi) observations of the ground surface obtained from a walkover survey with a GNSS instrument. These data were collected over the eastern part of Qikiqtaruk - Herschel Island, in the Canadian Yukon (69.5N, 138.8W). The images were collected in July and August 2016. Further details on the image processing are provided in the lineage section. This dataset was created by Andrew Cunliffe, with support from Isla Myers-Smith, Jakob Assmann, Jeffery Kerby and Gergana Daskalova (https://teamshrub.com/), in order to inform ongoing ecological monitoring studies in this area. This research was supported by the Natural Environment Research Council (NE/M016323/1), and the NERC Geophysical Equipment Facility (GEF:1063).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.264
Teacher spread0.244 · 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
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
Published2021
Admission routes1
Has abstractyes

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Same venueNERC Environmental Data ServiceFrench-language works237,207