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Record W2946180729 · doi:10.1002/bies.201800256

Evolutionary “Experiments” in Symbiosis: The Study of Model Animals Provides Insights into the Mechanisms Underlying the Diversity of Host–Microbe Interactions

2019· review· en· W2946180729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioEssays · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect symbiosis and bacterial influences
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthDeutsche ForschungsgemeinschaftNational Institute of General Medical SciencesCanadian Institute for Advanced Research
KeywordsBiologyHost (biology)Construct (python library)Diversity (politics)Evolutionary biologyMechanism (biology)Model organismComputational biologyEcologyGeneticsComputer scienceGeneEpistemologySociology

Abstract

fetched live from OpenAlex

Current work in experimental biology revolves around a handful of animal species. Studying only a few organisms limits science to the answers that those organisms can provide. Nature has given us an overwhelming diversity of animals to study, and recent technological advances have greatly accelerated the ability to generate genetic and genomic tools to develop model organisms for research on host-microbe interactions. With the help of such models the authors therefore hope to construct a more complete picture of the mechanisms that underlie crucial interactions in a given metaorganism (entity consisting of a eukaryotic host with all its associated microbial partners). As reviewed here, new knowledge of the diversity of host-microbe interactions found across the animal kingdom will provide new insights into how animals develop, evolve, and succumb to the disease.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.555
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.159
GPT teacher head0.332
Teacher spread0.173 · 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