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Record W2775563880 · doi:10.1017/9781316871867.016

Ecological Networks in Managed Ecosystems: Connecting Structure to Services

2017· book-chapter· en· W2775563880 on OpenAlexaff
Christian Mulder, Valentina Sechi, Guy Woodward, David A. Bohan

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEcosystemEcosystem servicesComputer scienceEnvironmental resource managementBusinessEcologyEnvironmental scienceWorld Wide WebBiology

Abstract

fetched live from OpenAlex

Introduction Ecological networks represent a cornerstone of ecology: they describe and evaluate the links between form and function in multispecies systems, such as food-web structure and dynamics, and they connect different scales and levels of biological organization (Moore and de Ruiter, 2012; Wall et al., 2015). These properties of being able to elucidate both the structure within complex systems and their scaling indicate that ecological networks and network theory could be widely applied to practical problems, including management decision-making processes such as the design of nature reserves and the preservation of ecosystem services. While the study of networks – initially food-web compartments, then community assemblages, and more recently mutualistic networks – is now firmly embedded in ecology (Levins, 1974; Cohen, 1978; Hunt et al., 1987; Beare et al., 1992; Solé and Montoya, 2001; Berlow et al., 2004; Moore et al., 2004; Cohen and Carpenter, 2005; Thébault and Fontaine, 2010; Moore and de Ruiter, 2012; Pocock et al., 2012; Neutel and Thorne, 2014), the application of such approaches to managed ecosystems has lagged far behind. There are many explanations for this disconnection between agro-ecology and ecology, not least the pervasive view that because they are human managed and disturbed agro-systems are fundamentally “unnatural” and different from natural ecosystems: most ecologists prefer to study so-called natural ecosystems, even though most of these have in fact been heavily influenced by mankind for centuries either directly by local activity or indirectly by long-distance pollution. Network approaches have rarely been applied to agriculture and forestry, which is perhaps surprising given that much of the early, integrated management research (e.g., from the seminal works by Von Carlowitz, 1713, and Von Liebig, 1840, onwards) and the study of networks that stimulated major advances in ecological theory was grounded in attempts to improve agricultural and timber production (Wardle, 2002; Schröter et al., 2003; Coleman et al., 2004; Moore and de Ruiter, 2012, and the references therein). The last two decades have seen a hiatus in advances in agro-ecology in this area, while new network theory and empirical studies have elucidated the roles of body size in ecosystems and the study of plant–pollinator networks and other mutualistic webs have redefined our understanding of general ecology.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.996
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2017
Admission routes1
Has abstractyes

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