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Exact models

2000· book-chapter· en· W4388318139 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.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMathematics
TopicAdvanced Mathematical Theories
Canadian institutionsYork University
Fundersnot available
KeywordsVariety (cybernetics)Focus (optics)Function (biology)Computer scienceMathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Abstract In this chapter a variety of directed models of walks, polygons and animals will be studied. These models include the partition polygons encountered in Chapter 3, as well as directed and partially directed walks. The fact that the models in this chapter are called “exact models “ implies that the generating function can, in may cases, be explicitly written down, often as an infinite product or a q-analogue of a special function, and in this way an “exact solution “ of the model is found. Careful analysis is then required to extract the free energies and other critical properties from the exact solutions of these models; this can be a challenging procedure. It is not possible to give more than an introduction to this active and vast area in a single chapter; instead, I shall focus on a few specific models which are directed versions of models considered elsewhere in this monograph, and whose solution will illuminate the general comments made in Chapters 2 and 3, as well as being related to models considered in later chapters.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.002

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 head0.088
GPT teacher head0.317
Teacher spread0.230 · 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

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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