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Record W2968292900 · doi:10.19189/map.2018.omb.364

Are point measurements in a bog representative of their surrounding area?

2019· article· en· W2968292900 on OpenAlexafffund
Sarah A. Howie, Ilja van Meerveld

Bibliographic record

VenueMires and Peat · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsEnvironment and Climate Change Canada
FundersUniversity of British Columbia
KeywordsBogEnvironmental sciencePeatPoint (geometry)Hydrology (agriculture)GeologySoil scienceEcologyGeotechnical engineeringMathematicsBiologyGeometry

Abstract

fetched live from OpenAlex

Descriptions of abiotic properties in bogs are often based on point measurements. To assess whether these point measurements are representative of their surrounding area, depth to water table (DTW), soil moisture, pH, electrical conductivity (EC), the degree of peat humification and ash content were measured at 25 points in a 4 m × 4 m study site. The gravimetric moisture content of the peat samples varied little (coefficient of variation (CV): 2–4 %), while the volumetric moisture content (CV: 11 %) and DTW (CV: 48 %) were more variable. Pore water pH also varied little throughout the study site (CV: 1 %), but pore water EC was more variable (CV: 84 %). The degree of humification was generally within 1–2 points on the von Post scale. Ash content was fairly variable (CV: 61–100 %). Plant species composition varied across the study site in relation to microtopography and was, not surprisingly, most strongly influenced by DTW and near-surface soil moisture. Some point measurements in bogs (e.g. pH, gravimetric moisture content) are likely to be representative for an area of at least several square metres, while other variables (e.g. EC, volumetric moisture content, degree of humification, ash content) may need to be measured at more than one point to obtain a representative average.

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)0.007
metaresearch head score (Gemma)0.017
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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.037
GPT teacher head0.255
Teacher spread0.219 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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