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Record W3214211952 · doi:10.1080/11956860.2021.1987653

Forest dependent birds are the main frugivorous species in mutualistic networks from the Brazilian Cerrado

2021· article· en· W3214211952 on OpenAlexvenueno aff
Adriano Marcos da Silva, Luís Paulo Pires, Celine de Melo

Bibliographic record

VenueEcoscience · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFrugivoreDefaunationSeed dispersalCentralityBiologyEcologyBiological dispersalHabitat

Abstract

fetched live from OpenAlex

Not all frugivore species are equally important to the structure and maintenance of mutualistic networks and identifying the most relevant species is of great relevance to conservation ecology. Centrality metrics provide insightful information on the relative contribution of individual fruit-eating species to the topology of the networks, but there is still considerable debate on what ecological traits make frugivores highly central. We aimed to identify whether frugivory level, gape width, and forest dependency are associated with the importance of fruit-eating birds to the networks. The relative contribution of individual bird species to the networks was calculated using a combination of three centrality metrics. A mixed-effects model showed that central species in the networks were highly connected forest specialist birds. Gape width and the proportion of fruits in the diet did not explain centrality. These findings are concerning because these birds we found to be the most important species are usually highly sensitive to human disturbances and because their selective defaunation may breakdown plant-frugivore mutualistic networks. Therefore, we suggest that the conservation of seed dispersal networks in forest fragments of the Cerrado should consider protecting forest specialized frugivorous birds.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.659

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.202
Teacher spread0.163 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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