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Record W1568137422

Polyface: The Farm of Many Faces

2010· article· en· W1568137422 on OpenAlexaff
Deishin Lee, Stephanie van Sice

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsWestern University
Fundersnot available
KeywordsLeverage (statistics)Value (mathematics)Diversity (politics)BiodiversitySustainable ValueContext (archaeology)AgricultureIndustrial organizationProcess (computing)BusinessScale (ratio)Value creationEnvironmental resource managementEnvironmental economicsEconomicsSustainabilityComputer scienceGeographyEcologyArtificial intelligencePolitical scienceCartography
DOInot available

Abstract

fetched live from OpenAlex

This case explores a method of value creation through exploiting synergies that exist in an environment where there is diversity. The context of the case is a farm where biodiversity is leverage to create value. This is contrasted to industrial farming which operates on the principles of economies of scale. The case also provides an opportunity for students to discuss the environmental impact of different types of operating systems.Learning Objective:Introduce a method of value creation through exploiting synergies between different types of process, that can be beneficial for the environment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0070.007
Open science0.0010.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.1370.021

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 head0.050
GPT teacher head0.367
Teacher spread0.317 · 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 designNot applicable
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

Citations3
Published2010
Admission routes1
Has abstractyes

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