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Record W7131755081

from Part II - Food Webs: From Traits to Ecosystem Functioning

2017· book-chapter· en· W7131755081 on OpenAlexaboutno aff
U. Gaedke, B. Beisner, A. Binzer, A. Downing, C. Guill, T. Klauschies, J. Kuiper, F. Soudijn, W. Mooij

Bibliographic record

VenueMPG.PuRe (Max Planck Society) · 2017
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTraitFlexibility (engineering)Trophic levelTerminologyFunctional ecologyPopulationEcosystemEcological systems theoryEnvironmental change
DOInot available

Abstract

fetched live from OpenAlex

Introduction Although the ubiquitous biodiversity-related flexibility of ecological systems is qualitatively well established, most empirical and theoretical studies regard ecological systems so far as units with rigid, predefined properties. The reason for this static approach is that incorporating the tremendous diversity and flexibility of natural systems into empirical and theoretical studies has been extremely challenging in terms of developing consistent mathematical frameworks and designing appropriate experiments. This approach has also been necessary owing to the lack of empirical data on the ability of species to change properties over time. A recent approach to solve this problem is to move from a species- to a trait-based perspective. This is not just a change in terminology but in concept, providing a mechanistic basis for biodiversity–ecosystem function relationships and improving our potential to identify general rules in community ecology (McGill et al., 2006; Savage et al., 2007; Hillebrand and Matthiessen, 2009). Functional traits are used to link species to their function in the ecosystem. They are well defined, measurable properties of individuals (e.g., edibility or diet selectivity) affecting their performance and responses to environmental changes and hence population and community dynamics as well as trophic interactions. The frequency distribution of functional traits (Figure 10.1a) enables a quantification of functional diversity. Large variation in trait values (e.g., a full range from highly edible, fast growing to almost inedible, slow growing species) implies a high functional diversity and vice versa. This trait distribution may be described by its shape and central tendency (Figure 10.1b) and may change in response to altered abiotic (e.g., temperature) and biotic conditions (e.g., predator density) and thus characterize the milieu with which individual organisms interact (McGill et al., 2006) (Figure 10.1c). Scientific Background Maintaining the different kinds of ecosystem services in a way that optimizes human well-being and economy is one of the most urgent tasks of our century, which challenges policy-makers as well as scientists. The frequency and intensity of land use, climate change, and other anthropogenically induced environmental disturbances are accelerating biodiversity declines worldwide. The negative impact of these processes on ecological systems (e.g., individuals, populations, communities, and food webs) may amplify each other: environmental changes can accelerate biodiversity loss and a reduced biodiversity may increase the sensitivity of ecological systems to environmental changes. © Cambridge University Press 2018.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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