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Record W4405994969 · doi:10.1177/09596836241307302

Historical archives from the Roman Monarchic and Republican periods show human perceptions of environment and climate change in Italy, 753–29 BCE

2025· article· en· W4405994969 on OpenAlexaff
Claudia Paparella, Seth Bernard, Mónica Bini, Andrea Columbu, Ilaria Isola, Kyle Harper, Ruben Post, Karin A F Zonneveld, Giovanni Zanchetta

Bibliographic record

VenueThe Holocene · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClimate changePerceptionHistoryArchaeologyAncient historyGeographyGeologyOceanographyBiology

Abstract

fetched live from OpenAlex

Historical archives for the Roman Monarchic and Republican periods (753–29 BCE) offer a highly resolved series of observations of environmental and climatic phenomena in Central Italy. This paper presents a new collection of these historical archives, gathering 319 observations across the period. We introduce the historical character of these archives and point out aspects affecting their analysis and interpretation for reconstruction of past environmental and climatic conditions in Italy in the latter half of the first millenium BCE. Archival information is seen to be generally reliable from the fifth century BCE onward, providing a valuable source about regional past climate. The historical archives’ anecdotal nature along with complexities of their formation and transmission encourage cautious and closely contextualized interpretation, and we advocate the use of this information most of all to understand Romans’ changing experience of environment and climate. We offer comparison of this data to current understanding of regional climate conditions based on scientific proxies, especially speleothems and marine cores. These records show some convergence with the historical archives, and we discuss the possibility that this may reflect a relatively warm, wet climate period (Roman Warm Period) in Italy coterminous with Rome’s initial phase of imperial expansion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.566

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 studies0.0000.001
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 head0.023
GPT teacher head0.228
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

Citations4
Published2025
Admission routes1
Has abstractyes

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