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Record W4410842323 · doi:10.1016/j.jasrep.2025.105246

Landscape affordances, GIS, and the hunting landscape of rock art in the Central Iranian Plateau

2025· article· en· W4410842323 on OpenAlexafffund
Ebrahim Karimi

Bibliographic record

VenueJournal of Archaeological Science Reports · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsAffordancePlateau (mathematics)GeographyArchaeologyCultural landscapeLandscape archaeologyLandscape designPhysical geographyGeologyEnvironmental resource managementComputer science

Abstract

fetched live from OpenAlex

Hunting is the most common narrative scene in the rock art of Iran. This prevalent use of hunting scenes, along with the potential use of some rock art regions for hunting purposes both today and in ancient times, have led to one main interpretation of rock art as depictions made by hunters and in relation to hunting activities in the Central Iranian Plateau. However, this assumption has not been thoroughly tested and is not explored on a broader landscape scale. Using a GIS-landscape approach, this study attempts to analyze the intersection of the petroglyphs, hunting blinds, and landscape affordances to reconstruct the hunting landscape of rock art in the Central Iranian Plateau. Rather than a chronological association, this approach emphasizes how the environmental affordances link the petroglyphs, hunting blinds, and hunting activities within the landscape of rock art. This suggests that the same locations and their capacities could have been recognized and used by different land users, whether contemporaneously or across different periods. Additionally, this paper discusses how the placement of petroglyphs enhanced the readability of the landscape’s affordances in relation to hunting and marked the key areas with hunting potential.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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