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Record W2171383855 · doi:10.1144/sp352.1

The role of geoarchaeology in extending our perspective

2011· article· en· W2171383855 on OpenAlexaff
Lucy Wilson

Bibliographic record

VenueGeological Society London Special Publications · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGeoarchaeologyPerspective (graphical)GeologyArchaeologyGeographyArtVisual arts

Abstract

fetched live from OpenAlex

Abstract Specialists and the general public alike are very aware of human impacts on our environment. Climate change, deforestation, desertification, soil erosion and other topics are currently much in the news, but human influence on the environment is not a new phenomenon. Geoarchaeologists study the traces of human interactions with the geosphere dating back to ancient times, as well as up to and in the present. Geoarchaeological investigations provide the key to recognizing landscape and environmental change within a region, as well as reconstructing ancient landscapes and palaeoclimatic regimes. Such an interdisciplinary approach makes it possible to interpret the ways that humans affect the geosphere, through such things as subsistence and resource exploitation activities, settlement location, and local and regional land-use patterns. This approach also allows us to determine the effects of environmental change on human societies. For millennia, humans have been coping with, or provoking, environmental change. We have exploited, extracted, over-used but also in many cases nurtured the resources that the geosphere offers. In the geoarchaeological perspective, human life has never been separate from nature. Geoarchaeology can thus provide a more inclusive and longer-term view of human–geosphere interactions, and serve as a valuable aid to those who try to determine sustainable policies for the future.

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 score0.180
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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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