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Vegetation recolonisation of abandoned agricultural terraces on Antikythera, Greece

2010· article· en· W2085250859 on OpenAlexaff
Carol Palmer, Sue Colledge, Andrew Bevan, James Conolly

Bibliographic record

VenueEnvironmental Archaeology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent University
FundersArts and Humanities Research CouncilResearch Councils UK
KeywordsGeographyAgricultureAgroforestryVegetation (pathology)PopulationMediterranean climateShifting cultivationAgricultural landGrasslandEcologyArchaeologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Antikythera is a small, relatively remote Mediterranean island, lying 35 km north-west of Crete, and its few contemporary inhabitants live mainly in the small village at the only port. However, an extensive network of terraces across the island bears witness to the past importance of farming on the island, although the intensity of use of these cultivated plots has changed according to fluctuating population levels. Most recently, the rural population and intensity of cultivation have dramatically declined. Our aim is to understand the recolonisation process of agricultural land by plants after terraces are no longer used for the cultivation of crops. The results demonstrate a relatively quick pace of vegetative recolonisation, with abandoned farm land covered by dense scrub within 20 to 60 years. The archaeological implications are that, following even relatively short periods of abandonment, the landscape would have required arduous reinvestment in the removal of scrub growth, as well as the repair and construction of stone terraces, to allow cultivation once again.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

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)0.0020.000

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 head0.005
GPT teacher head0.175
Teacher spread0.170 · 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

Citations24
Published2010
Admission routes1
Has abstractyes

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