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Record W2033363940 · doi:10.3732/ajb.94.1.79

Species richness along a production gradient: a multivariate approach

2007· article· en· W2033363940 on OpenAlexafffundabout
Guillaume de Lafontaine, Gilles Houle

Bibliographic record

VenueAmerican Journal of Botany · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpecies richnessMultivariate statisticsProductivityEcologyBiologyPoolingField (mathematics)Range (aeronautics)Production (economics)StatisticsMathematics

Abstract

fetched live from OpenAlex

The specific shape of the relationship between plant diversity and productivity and the causal mechanism(s) behind the observed pattern(s) are still highly debated. Recent advances suggest that the relationship depends on several environmental variables and may change with the observational scale. In this study, a multivariate, multiscale approach was used to identify the variables that determine the relationship between species richness and annual production along a forest/old field edge in southern Québec (Canada). Various relationships between richness and production were found at different distances to the edge. In the forest, most relationships were positive and linear, while in the old field the relationship shifted from positive linear to non-significant with increasing distance from the edge. In the forest or in the old field, the shape of the relationship (all distances from the edge combined) was unimodal. Path analyses showed that species richness was determined mostly by production, which was influenced by different limiting resources, depending on the community (forest or old field). An increasing range in production created by pooling across community types can confound the resources and/or conditions determining the diversity-productivity relationship.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 teacher head, 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

Citations11
Published2007
Admission routes3
Has abstractyes

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