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Record W2007932007 · doi:10.1080/01431160701253220

Estimation of grassland CO<sub>2</sub>exchange rates using hyperspectral remote sensing techniques

2007· article· en· W2007932007 on OpenAlexaffabout
S. C. Black, Xulin Guo

Bibliographic record

VenueInternational Journal of Remote Sensing · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEnvironmental scienceHyperspectral imagingRemote sensingGrasslandVegetation (pathology)Carbon sinkPhotochemical Reflectance IndexReflectivitySoil scienceEcosystemNormalized Difference Vegetation IndexAtmospheric sciencesLeaf area indexEcologyGeographyGeology

Abstract

fetched live from OpenAlex

Although the link between CO2 exchange and spectral reflectance is well established in laboratory conditions, limited research has been conducted in the field. To determine the applications of remote sensing in the estimation of the northern mixed grasslands as a carbon sink, the primary objective of this study was to evaluate several narrow‐band vegetation indices and band depth analysis in the prediction of leaf CO2 exchange rates in a northern mixed grass prairie ecosystem. Spectral reflectance and CO2 exchange measurements were collected from 13 sites located in Grasslands National Park, Saskatchewan, Canada. Pearson's correlation found a significant relationship between the CO2 exchange rates and the Photochemical Reflectance Index (PRI). Linear regression showed that the PRI explained 46% of the variance seen in the leaf CO2 exchange rates. This is somewhat lower than previous experiments conducted in laboratory conditions; however, the current study was conducted in field conditions, where there are a number of different species in the field of view and background effects from soil, litter and dead materials, which are negligible in laboratory conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.281
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2007
Admission routes2
Has abstractyes

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