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Record W4319227016 · doi:10.1007/s12520-023-01724-5

How animal dung can help to reconstruct past forest use: a late Neolithic case study from the Mooswinkel pile dwelling (Austria)

2023· article· en· W4319227016 on OpenAlexfundno aff
Thorsten Jakobitsch, Cyril Dworsky, Andreas G. Heiss, Marlu Kühn, Sabine Rosner, Jutta Leskovar

Bibliographic record

VenueArchaeological and Anthropological Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersÖsterreichischen Akademie der WissenschaftenUniversity of TorontoUniversität Basel
KeywordsFodderForageBiologyEvergreenLivestockHayGeographyEcologyAgronomy

Abstract

fetched live from OpenAlex

Abstract Animal dung analyses are a useful tool for vegetational studies. Preserved ruminant dung from archaeological layers offers a unique possibility for the reconstruction of past fodder management strategies, and further for studying the impact of fodder acquisition and pasturing on forests. In this case study we investigate the impact of Late Neolithic livestock keeping on the forest around the “Mooswinkel” pile dwelling at the Austrian lake Mondsee through the analysis of botanical macroremains, insect remains as well as microhistological analyses of botanical remains in animal dung. Seasonal plant parts in the dung point out that cattle, goats, and/or sheep were evidently kept inside the settlement during the winter for protection. During the daytime, they were allowed to forage around the settlement. Winter fodder consisted of dried leaf hay, hay from grasses and herbs, male flowers of early blooming bushes, and fresh twigs of evergreen species, such as fir ( Abies alba ), ivy ( Hedera helix ), and mistletoe ( Viscum album ).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.006
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.092
GPT teacher head0.272
Teacher spread0.180 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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