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Record W1571294517 · doi:10.1002/9780470027318.a0867m

Soil Instrumental Methods

2000· other· en· W1571294517 on OpenAlex
M.R. Carter, D. Curtin, E. G. Gregorich

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.

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2000
Typeother
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSoil nutrientsEnvironmental scienceSoil waterSampling (signal processing)NutrientSoil qualityPlant growthSoil testSoil scienceComputer scienceEcologyAgronomyBiology

Abstract

fetched live from OpenAlex

Abstract Instrumental methods play an important role in the analysis of soil material. Soil is composed of mineral, organic, water, and air components. Analytical methods are required that characterize the fitness or quality of soils to perform various functions, such as providing a medium for plant growth, recycling waste products, and regulating and storing water, energy, and nutrients. The objective of this article is to describe the basic concept and approach to laboratory analysis of soil, and to outline the application of techniques used in the analysis of soil quality properties. Emphasis is placed on soil sampling and sample preparation, followed by a description of the possible instrumental methods available for the analysis of the chemical, biochemical, and physical properties of soil.

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)0.000
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 score0.301
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.3030.001

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 head0.008
GPT teacher head0.275
Teacher spread0.267 · 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