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Record W6906159847 · doi:10.17026/ar/q0in4j

AWN-opgraving Katwijk

2024· dataset· nl· W6906159847 on OpenAlexaff

Bibliographic record

VenueDANS Data Station Archaeology · 2024
Typedataset
Languagenl
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsKelowna General Hospital
Fundersnot available
KeywordsRural developmentOpen air

Abstract

fetched live from OpenAlex

Tussen 12 september en 20 oktober vond een archeologische opgraving plaats op een door de gemeente Katwijk vrijgegeven bouwterrein. De opgraving, geleid door AWN afdeling 6 (Rijnstreek) met financiële steun van de gemeente Katwijk, provincie Zuid-Holland en het bestuur van de AWN, werd uitgevoerd onder ideale weersomstandigheden. Vrijwilligers, waaronder studenten van de Universiteit Leiden, werkten samen met professionele archeologen van ADC ArcheoProjecten, gebruikmakend van geavanceerde technologieën zoals een total station voor efficiënt inmeten van de sporen. De site onthulde meerdere boerderijen uit de Merovingische tijd (circa 500-700), die in fases over elkaar heen gebouwd waren op een hoger gelegen terrein. Het publiek kon de opgravingen ook bezoeken tijdens evenementen zoals de Nationale Archeologiedagen. De vondsten, waaronder versierd aardewerk en scherven van glas uit de Merovingische tijd, worden verder verwerkt en geanalyseerd in de werkruimte van de AWN Rijnstreek in Alphen aan den Rijn. Dit project markeert een succesvolle samenwerking tussen vrijwilligers, professionals en academici, en inspireert tot meer dergelijke initiatieven in de toekomst.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.285
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0080.004
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.2850.128

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 head0.034
GPT teacher head0.311
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

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