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Conservation Science

2018· other· en· W4238160473 on OpenAlexaff
Alison D. Murray, Amandina Anastassiades

Bibliographic record

VenueThe Encyclopedia of Archaeological Sciences · 2018
Typeother
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsCultural heritageContext (archaeology)ConservationInterpretation (philosophy)Object (grammar)Conservation scienceCultural heritage managementIdeal (ethics)Field (mathematics)Data scienceEngineering ethicsComputer scienceGeographyArchaeologyEngineeringEpistemologyEnvironmental planningEcologyMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Conservation science, also known as heritage science, is an interdisciplinary field that focuses on preserving artistic and cultural heritage objects and contributes to an understanding of their context. Characterizing individual materials and their deterioration yields knowledge of the structure and properties of a heritage object and, along with identifying degradation pathways, furthers development of conservation treatments. Research also determines the ideal environmental conditions for heritage objects, for example, appropriate relative humidity and temperature ranges and lighting conditions. Recommendations for appropriate materials for exhibiting, storing, and transporting objects as well as for use in conservation treatments are also based on conservation science research. Conservation scientists specialize in specific analytical techniques and apply expert knowledge of sampling, sample preparation, data interpretation, and the significance of the results for cultural heritage. These professionals work closely with others, for example conservators, archaeologists, art historians, and curators.

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)0.004
metaresearch head score (Gemma)0.012
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: Other · Consensus signal: Other
Teacher disagreement score0.144
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0040.005
Scholarly communication0.0120.006
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1440.034

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 head0.052
GPT teacher head0.284
Teacher spread0.232 · 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
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

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