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Record W253496891 · doi:10.21425/f5fbg12418

update: Emerging research opportunities in global urban ecology

2012· article· en· W253496891 on OpenAlexaboutno aff
Frank A. La Sorte

Bibliographic record

VenueFrontiers of Biogeography · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyUrban ecologyMacroecologyGeographyBiologyEnvironmental resource managementBiogeographyUrbanizationEnvironmental science

Abstract

fetched live from OpenAlex

ISSN 1948‐6596 References Beck J., & Sieber, A. (2010) Is the spatial distribution of mankind’s most basic economic traits deter‐ mined by climate and soil alone? PLoS ONE 5(5): e10416. Burke, M., Miguel, E., Satyanath, S., Dykema, J. & Lo‐ bell, D. (2009) Warming increases risk of civil war in Africa. Proceedings of the National Acad‐ emy of Sciences USA, 106, 20670–20674. Diamond, J. (2005) Collapse: how societies choose to fail or succeed. Viking. Hsiang, S.M., Meng, K.C. & Cane, M.A. (2011) Civil con‐ flicts are associated with the global climate. Na‐ ture, 476, 438–411. Samson, J., Berteaux, D., McGill, B.J., Humphries, M.M. (2011) Geographic disparities and moral hazards in the predicted impacts of climate change on human populations. Global Ecology and Bio‐ geography, 20, 532–544. news and update Zhang, D.D., Lee, H.F., Wang, C., Lie, B., Pei, Q., Zhang, J. & An, Y. (2011) The causality analysis of cli‐ mate change and large‐scale human crisis. Pro‐ ceedings of the National Academy of Sciences USA, 108, 17296–17301. Zhang, D.D., Brecke, P., Lee, H.F., He, Y.‐Q. & Zhang, J. (2007) Global climate change, war and popula‐ tion decline in recent human history. Proceed‐ ings of the National Academy of Sciences USA, Edited by Richard Ladle update Emerging research opportunities in global urban ecology Biogeographers have examined how human activi‐ ties have affected patterns of biological diversity from a variety of perspectives, with special atten‐ tion often given to oceanic islands. With the cur‐ rent accelerating pace of environmental change, these effects are increasingly evident at global scales. Human industry, commerce, agriculture and transportation all have the potential now to affect natural systems globally through an assort‐ ment of drivers; primary among these are land‐ use change, species introductions and climate change. Human activities and their consequences come to a unique focus in urban areas, an expand‐ ing form of land use that is attracting increasing research attention from ecologists (Grimm et al. 2008). Urban areas contain similar environmental conditions worldwide and act as a focal point for species introductions and extinctions. These hu‐ man‐dominated environments offer unique op‐ portunities to investigate the broad‐scale dynam‐ ics of human‐mediated biotic interchange (La Sorte et al. 2007), its consequences for β diversity (La Sorte et al. 2008) and the regional factors and biological traits associated with native species ex‐ tinctions (Hahs et al. 2009, Duncan et al. 2011). Urban areas typically contain spatially heteroge‐ neous collections of native and non‐native species (McKinney 2008); these unique assemblages can be examined based on their compositional (Niemela et al. 2002) and phylogenetic structures (Ricotta et al. 2009). Three nested sampling ap‐ proaches are currently used to investigate urban systems at broad spatial scales: urban plots or transects, the entire urban matrix and the urban matrix embedded within a regional context (Werner 2011). Each sampling approach provides a unique inferential basis, although the third al‐ lows for more refined interpretation, controlling for regional differences. A recent study in Global Ecology and Bio‐ geography adopts a novel perspective and exam‐ ines how avian assemblages sampled within plots of intact vegetation in urban and semi‐natural ar‐ eas differ based on several common mac‐ roecological relationships. Pautasso et al. (2011) compiled data on species composition and abun‐ dance from all around the globe, although the majority of the samples are from Europe and North America. A primary finding of the study was a lack of evidence for differences in the species– area, species–abundance or species–biomass rela‐ frontiers of biogeography 3.3, 2011 — © 2011 the authors; journal compilation © 2011 The International Biogeography Society

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 score0.054
Threshold uncertainty score0.461

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 studies0.0000.000
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 head0.033
GPT teacher head0.277
Teacher spread0.243 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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