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Record W3104770894 · doi:10.17863/cam.59608

Chapter 2 - Chronology and stratigraphy of the valley systems (Temple landscapes Fragility, change and resilience of Holocene environments in the Maltese Islands)

2020· book-chapter· en· W3104770894 on OpenAlexfundno aff
Chris Hunt, Michelle Farrell, Katrin Fenech, Charles French, Rowan McLaughlin, Maarten Blaauw, Jeremy Bennett, Rory Flood, Sean Pyne-O’Donnell, Paula Reimer, Alastair Ruffell, A.J. Cresswell, T. Kinnaird, D.C.W. Sanderson, Sean Taylor, Caroline Malone, Simon Stoddart, Nicholas C. Vella

Bibliographic record

VenuePure (Coventry University) · 2020
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersMagdalene College, University of CambridgeQueen's University BelfastEuropean CommissionQueen's UniversityUniversity of Cambridge
KeywordsChronologyStratigraphyGeologyArchaeologyPaleontologyGeography

Abstract

fetched live from OpenAlex

Adequate absolute dating is critical to understanding the past, especially where data concerning environmental changes from different sites are being compared, as chronology is often the only reliable way to compare evidence from multiple contexts. The archaeological chronology of Malta is becoming increasingly well resolved and this sets up a challenge – how can we obtain comparable high-resolution chronologies of environmental change in the Maltese Islands? The Maltese landforms pose significant barriers to achieving this goal, as much of the available palaeoenvironmental evidence is limited to cores in alluvial or shallow-marine sediments, which contain materials that have been subject to much re-working through time. In this chapter, we introduce and discuss the various techniques that the FRAGSUS Project has brought to bear on this problem, and review the main approaches used. [excerpt]

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.429

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 head0.031
GPT teacher head0.194
Teacher spread0.163 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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