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Record W4411404983 · doi:10.1017/9781009158688.010

Spatial Diversity Analysis

2025· book-chapter· en· W4411404983 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2025
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSpatial and Panel Data Analysis
Canadian institutionsUniversity of TorontoUniversity of Northern British Columbia
Fundersnot available
KeywordsDiversity (politics)GeographyAntenna diversityComputer scienceSociologyAnthropologyTelecommunications

Abstract

fetched live from OpenAlex

Spatial structure is key to understanding diversity in ecological systems, being affected by both location and scale. The effects of scale are often dealt with as the hierarchy of alpha (local area), beta (between areas) and gamma (largest areas) diversity. All have spatial aspects, but beta diversity may be most interesting for spatial analysis because it involves complex responses such as intermediate-scale nestedness and species turnover with or without environmental gradients. In addition to species diversity within communities, the diversity of species composition or combinations as a function of location is an important characteristic of ecological assemblages. Many aspects of spatial diversity are best understood by spatial graphs, with sites as nodes and edges quantifying inter-site relationships. Temporal information, when available, can provide crucial insights about spatial diversity through understanding the dynamics of the system.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0280.007

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 head0.034
GPT teacher head0.181
Teacher spread0.146 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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