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Record W3090930026 · doi:10.1002/bes2.1763

Spatial Variation in Community Structure and the Underlying Environmental Variation

2020· article· en· W3090930026 on OpenAlexaboutno aff
Alexis M. Catalán, Nelson Valdivia, Ricardo A. Scrosati

Bibliographic record

VenueBulletin of the Ecological Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
Fundersnot available
KeywordsVariation (astronomy)Spatial variabilityIntertidal zoneSpatial ecologyIntertidal ecologyScale (ratio)Environmental scienceTemporal scalesGeographic variationEcologyCommunity structureRocky shorePhysical geographyGeographyOceanographyGeologyBiologyCartographyStatisticsDemography

Abstract

fetched live from OpenAlex

Rocky intertidal systems are useful to study the influence of measurement scale on spatial biological variation in community structure. Vertical biological variation across elevations (triggered by tides) has typically been found higher than horizontal (alongshore) variation at local scales but lower than horizontal variation at regional scales. In those studies, alongshore environmental variation increased with scale. We surveyed the Canadian and Chilean coasts at those scales but with a limited underlying alongshore environmental variation and found that horizontal biological variation was never higher than vertical biological variation. Predicting spatial scales of biological variation should thus consider the underlying environmental variation. These photographs illustrate the article “Interhemispheric comparison of scale-dependent spatial variation in the structure of intertidal rocky-shore communities” by Alexis M. Catalán, Nelson Valdivia, and Ricardo A. Scrosati published in Ecosphere. https://doi.org/10.1002/ecs2.3068.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.184
Teacher spread0.170 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueBulletin of the Ecological Society of AmericaSame topicMarine and coastal plant biologyFrench-language works237,207