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Record W4240799072 · doi:10.14351/0831-4985-28.1.35

Dangers to malacological collections: Bynesian decay and pyrite decay

2014· article· en· W4240799072 on OpenAlexvenueno aff
Daniel C. Cavallari, Rodrigo B. Salvador, Bruno Rodrigues da Cunha

Bibliographic record

VenueCollection Forum · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPyriteRelative humidityHumidityCarbonateSulfuric acidFormic acidChemistryMineralogyEnvironmental chemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Specimen deterioration in museum collections and exhibits is a recurring worry of curators. Malacological collections, i.e., those containing mollusk shells, are of special concern, because these specimens are composed largely of calcium carbonate, a compound that easily deteriorates in acidic storage media. There are two main problems related to the presence of volatile acids in collections: Bynesian decay and pyrite decay. In Bynesian decay, acetic and formic acids, among others, are released by the materials used to store the collection: wood, varnishes, resins, paper, cardboard, and other storage materials. Under high relative humidity and inappropriate temperature, the acids react with the shells' carbonate, yielding salts and carbon dioxide. Pyrite decay is similar in appearance: fossil specimens may contain pyrite (FeS2), which in high relative humidity is oxidized, yielding sulfuric acid and other products. This acid corrodes other shells, including carbonatic fossils. The damage caused is irreversible, but there are methods to avoid or mitigate it. The collection should be kept in conditions of low relative humidity (45–50%) and appropriate temperature range (16–21°C). The use of archival materials, such as steel drawers and cabinets with electrostatic coating, is mandatory. Specimens containing reactive pyrite must be kept in archival-quality plastic containers or closed glass containers. Damaged specimens must be immediately removed and only returned to the collection after proper cleaning.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.006

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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

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