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Record W114151515 · doi:10.5751/es-00371-060102

A Fractal Landscape Realizer for Generating Synthetic Maps

2002· article· en· W114151515 on OpenAlexvenueno aff
William W. Hargrove, Forrest M. Hoffman, Paul M. Schwartz

Bibliographic record

VenueConservation Ecology · 2002
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsFractalGeographyComputer scienceCartographyMathematics

Abstract

fetched live from OpenAlex

A fractal landscape realizer has been developed that generates synthetic landscape maps to user specifications. The alternative landscape realizations are not identical to the actual maps after which they are patterned, but are similar statistically (i.e., the areas and fractal character of each category are replicated). A fractal or self-affine pattern generator is used to provide a spatial probability surface for each category in the synthetic map. The Fractal Realizer arbitrates contentions among categories in a way that makes it possible to preserve the fractal patterns of all the categories in the resulting synthetic landscape. Each synthetic landscape is one equally likely realization from among an infinite ensemble of possible fractal landscape combinations. Synthetic landscapes produced by the Fractal Realizer have been tested using a variant of the Turing Test. More than 100 map experts were presented with a series of 20 selections of paired maps, and asked to distinguish the real map from the synthetic realization. The resulting population of scores was not significantly different from a random binomial, suggesting that the experts were unable to discern the synthetic maps from the actual ones. Statistical landscape indices computed for 25 different synthetic realizations are compared with the values computed for the actual maps. The Fractal Realizer can be used as a stochastic generator of synthetic input maps to a spatially explicit simulation model to test the effects of landscape rearrangement on the uncertainty of model parameter estimates. The sensitivity of stochastic spatial simulations to prescribed input landscapes can be evaluated by supplying them with a series of synthetic maps that obey particular statistical characteristics and by monitoring changes in selected output responses. Statistically similar input landscapes with different spatial arrangements can be generated and supplied to spatial models as a hedge against pseudoreplication.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.032
GPT teacher head0.240
Teacher spread0.207 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations78
Published2002
Admission routes1
Has abstractyes

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