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Modifying modifiers: what happens when interspecific interactions interact?

2011· article· en· W1642515457 on OpenAlexaff
Antonio J. Golubski, Peter A. Abrams

Bibliographic record

VenueJournal of Animal Ecology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTrophic levelInterspecific competitionEcologyFood webBiologyEvolutionary biology

Abstract

fetched live from OpenAlex

1. The strength of the trophic link between any given pair of species in a food web is likely to depend on the presence and/or densities of other species in the community. How these trophic interaction modifications (TIMs) interact with one another to produce a net modifying effect is an important but under-explored issue. 2. We review several specific types of TIMs that are well understood to address whether the magnitude of the net modification changes with the number of modifiers, and whether modifiers usually increase or decrease each other's effects. 3. Modifications of interactions are generally not independent. It is likely that TIMs interact antagonistically in the majority of cases; the magnitudes of TIMs decrease as more modifiers are added, or new TIMs reduce the magnitudes of modifications that are already present. 4. Individual modifications are likely to have a smaller effect in many-species systems than expected from independent combination of modifications measured in systems with relatively few species. Thus, models that lack explicit TIMs may in some cases yield adequate predictions for species-level perturbations, provided that the net effects of TIMs are implicitly included in measured interaction strengths. 5. Many types of TIMs share structural similarities. Nevertheless, a complete understanding of their effects may require theory that distinguishes different 'functional groups' of modifiers and addresses how these are structured according to trophic relationships.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.260
Teacher spread0.114 · 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 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

Citations69
Published2011
Admission routes1
Has abstractyes

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