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Measurement Techniques

2018· book-chapter· en· W4249344024 on OpenAlex
Allan Howard, Aston Chipanshi, R. L. Desjardins, Andrii Kolotii, Nataliia Kussul, Heather McNairn, Sergii Skakun, Андрій Шелестов

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgronomy monograph/Agronomy · 2018
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEnvironmental scienceCarbon footprintAgricultural productivityAgricultureWater contentAgricultural engineeringGreenhouse gasFootprintFood securityWarning systemFlood mythEnvironmental resource managementEngineeringGeography

Abstract

fetched live from OpenAlex

This chapter aims to identify key state of the art operational or near-operational techniques and discusses them at scales that are relevant to agricultural production. It covers soil moisture measurement techniques that are available over a range of scales. Soil moisture is a key variable for crop productivity, crop management practices, flood and excess moisture risk, and is a controlling factor in greenhouse gas emissions from farming operations. Crop condition and drought monitoring techniques have been used as early warning for production and food security issues. Quantification of drought severity was originally based on meteorological and/or hydrological data. The chapter presents two case studies that apply the concepts and techniques toward measurement of the elements. The case studies presented are crop condition assessment in the Ukraine and the carbon footprint of beef in Canada.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.207
Teacher spread0.188 · 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