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Record W2805916100 · doi:10.4095/293342

Peak season leaf area index for the Nanaimo Aquifer - 2011

2013· report· en· W2805916100 on OpenAlexaffabout
M Maloley, Richard Fernandes, Francis Canisius, Christopher R. Butson

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicEnvironmental and biological studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsIndex (typography)AquiferEnvironmental scienceHydrology (agriculture)Leaf area indexForestryGeologyGeographyAgronomyGeotechnical engineeringGroundwaterBiologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This document describes the production and assessment of peak season leaf area index estimates over the Nanaimo Aquifer and surrounding regions from satellite imagery using standard Canada Centre for Remote Sensing algorithms. A 30m resolution map of 2011 peak season leaf area index for the Nanaimo Aquifer was created using a combination of in situ LAI estimates, vegetation indices from remotely sensed spectral data and land cover information from SPOT 5 satellite imagery. A total of 104 ground plots were sampled across the study area between July 15-30, 2011for the purposes of calibration and validation of the leaf area index product.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

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

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 head0.051
GPT teacher head0.248
Teacher spread0.197 · 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
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

Citations0
Published2013
Admission routes2
Has abstractyes

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