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Record W2977755673 · doi:10.1371/journal.pone.0222680

Quandong stones: A specialised Australian nut-cracking tool

2019· article· en· W2977755673 on OpenAlexfundno aff
Colin Pardoe, Richard Fullagar, Elspeth Hayes

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
FundersAustralian Research CouncilBarrick Gold Corporation
KeywordsAridCrackingGeographyStructural basinGeologyMaterials scienceGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

The quandong or native peach (Santalum acuminatum R.Br.) has been recognised as an important and tasty food resource among Aboriginal Australians in arid and semi-arid areas of southern Australia. It is valued for its fruit that is consumed raw or dried, and for its kernel, which is eaten raw or ground into paste for medicinal and skin care purposes. This paper reports on a study of ground stone implements within the Murray Darling Basin that has identified quandong stones as a distinct type of implement made specifically for the efficient cracking of quandong nuts. Data are presented on 1,327 ground stone implements from collections in 12 different locations in the Murray-Darling Basin (MDB), an area almost completely devoid of stone sources. Given the paucity of stone, multi-purpose use of implements is widely documented. Although it was common to find pits present in mortars and other ground stone tools demonstrating multiple functions, including use as anvils, a class of single purpose stones with multiple pits and distinctive form was identified. Most of these were found in areas known for groves of quandong and four were analysed for use-wear and residues along with two other ground stone items from the MDB. The results support their identification as specialised anvil stones for cracking quandong nuts.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.086
GPT teacher head0.290
Teacher spread0.205 · 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 designQualitative
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

Citations19
Published2019
Admission routes1
Has abstractyes

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