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Record W2312140810 · doi:10.1002/ecs2.1266

A network approach reveals surprises about the history of the niche

2016· article· en· W2312140810 on OpenAlexafffund
Michael T. Pedruski, Gregor F. Fussmann, Andrew Gonzalez

Bibliographic record

VenueEcosphere · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNicheEcologyNiche constructionNiche segregationEcological nicheUnificationEnvironmental niche modellingHistorical ecologyConceptual frameworkBiologySociologyComputer scienceHabitatSocial science

Abstract

fetched live from OpenAlex

Abstract The ecological niche is a prominent theoretical concept in many ecological fields, central to ecological understanding of species interactions and community structure. To better understand this important concept, and the impact it has had on ecology, we used a citation analysis to examine the history of the niche through citation patterns during the 20th century. In particular, we sought to document the spread of the niche across ecological subdisciplines, to evaluate whether the existence of different niche definitions facilitated the spread of the niche, and to see whether the conceptual integration stemming from adoption of the niche has also yielded an integration of the niche literature across subdisciplinary boundaries. We show that the ecological niche has been adopted by a number of subdisciplines, but that this success does not appear to have relied strongly on the different niche definitions, nor has it led to general integration of the niche literature across subdisciplinary boundaries. Our analysis thus not only examines the history of one of ecology's central concepts but also suggests that despite the conceptual unification that resulted from the broad adoption of the niche, a unified niche literature had not emerged by the close of the 20th century.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1160.001

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 head0.030
GPT teacher head0.205
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2016
Admission routes2
Has abstractyes

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