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Record W2016393291 · doi:10.1111/lre.12086

Water‐related bird assemblages in an urban pond ‘archipelago’: Winter patterns of bird species occurrence, abundance and richness

2015· article· en· W2016393291 on OpenAlexfundno aff
Maria Paola Di Santo, Giuseppe M. Carpaneto, Corrado Battisti

Bibliographic record

VenueLakes & Reservoirs Science Policy and Management for Sustainable Use · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersMcGill University
KeywordsSpecies richnessAbundance (ecology)ArchipelagoEcologyGeographyAssemblage (archaeology)Relative species abundanceCommon speciesBiologyHabitat

Abstract

fetched live from OpenAlex

Abstract This study reports on the patterns of species occurrence, abundance and richness of a wintering water‐related bird assemblage in an ‘archipelago’ of 70 small artificial urban ponds ( AUP s) embedded in a metropolitan landscape (Rome, central Italy). A total of 20 species in 26 AUP s were sampled. Only the largest AUP s (>0.1 ha) contained all these species, except for Gallinula chloropus . The highest total mean species abundance was observed in the largest ponds, with statistically significant differences evident among size classes. Two significant spatial thresholds in species abundance and richness were observed (between 0.01 and 0.1 ha; between 0.1 and 1 ha in size). The abundance of single species was correlated with their frequency of occurrence. Ponds in urban areas must be larger than 0.1 ha to host a rich winter assemblage of birds, with a further increase in richness noted with a surface area larger than 1 ha. The highest number of species was observed in the larger ponds (>1 ha). The species richness of each AUP is directly correlated to their size (log‐transformed species–area relationship: log S = 3.515 + 0.497 log A ; R 2 = 0.76). Further research should be conducted to confirm these patterns and to implement information useful for planning and management of artificial ponds in urban areas for this purpose.

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 score0.132
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.032
GPT teacher head0.286
Teacher spread0.254 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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