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Record W2125161469 · doi:10.1177/0959683612472001

Phytolith evidence of mid-Holocene Capsian subsistence economies in North Africa

2013· article· en· W2125161469 on OpenAlexaff
Julie Shipp, Arlene M. Rosen, David Lubell

Bibliographic record

VenueThe Holocene · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhytolithSubsistence agricultureHoloceneGeographyPastoralismForagingEcologyWetlandSubsistence economyClimate changeAridAgricultureArchaeologyLivestockBiologyForestryPollen

Abstract

fetched live from OpenAlex

Climatic fluctuations that occurred in North Africa during the early and middle Holocene had a profound impact on the environment of the region and would have required human populations in the area to adapt their subsistence and economic strategies in equally significant ways. Capsian groups, located in eastern Algeria and southern Tunisia from approximately 10,000 to 6000 cal. BP, were among the last North African foragers at a time when other groups were abandoning food collection to engage in food production in the form of pastoralism. Capsian foragers relied heavily on land snails, but we have little information on their use of plant resources, which can be an important indicator of economic adaptation to environmental change. In this study we use phytolith analyses at the Capsian site of Aïn Misteheyia in eastern Algeria to track the changes in subsistence strategies throughout much of the middle-Holocene climatic transitions. Our results show that Capsian foragers exploited plants such as sedges and small-seeded grasses from wetland microenvironments within their home ranges which allowed them to demonstrate robust and resilient resource procurement strategies, and maintain a foraging lifestyle resistant to major fluctuations in climate.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.242
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2013
Admission routes1
Has abstractyes

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