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Record W4384470495 · doi:10.24043/001c.83296

Forgotten Islands of the Past: The Archaeology of the Northern Coast of the Arabian Sea

2023· article· en· W4384470495 on OpenAlexvenueno aff
Paolo Biagi

Bibliographic record

VenueIsland Studies Journal · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
FundersMinistero degli Affari Esteri e della Cooperazione Internazionale
KeywordsIndusPrehistoryRadiocarbon datingArchaeologyHolocenePeriod (music)GeologyGeographyOceanographyPaleontology

Abstract

fetched live from OpenAlex

The Indus Delta plays an important role in the archaeology of the northern coast of the Arabian Sea. Little was known of this region until a few decades ago. The first surveys were carried out in the 1970s and were resumed by the present author in the 2010s. They have shown the great potential of the area for the interpretation of sea-level rise and its related human settlement between the beginning of the Holocene and the Hellenistic period. In this territory, several limestone terraces rise from the alluvial plain of the River Indus, which were islands in prehistoric and early historic times. Many archaeological artefacts, along with marine and mangrove shells, have been recovered from their surface and radiocarbon dated. These discoveries help us to follow the events that took place in the region in well-defined periods and interpret some aspects of the prehistoric coastal settlement in relation to the advance of the Indus Fan and the retreat of the Arabian Sea. The following questions are addressed in this paper: who settled these islands, when and why? During which prehistoric periods were mangrove and marine environments exploited? And what were the cultural characteristics of the communities that seasonally or permanently settled some of the present ‘rocky outcrops’?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.029
GPT teacher head0.240
Teacher spread0.211 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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