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Record W2319214401 · doi:10.3136/nskkk.60.257

Culture Collections in Japan and their Services

2013· article· en· W2319214401 on OpenAlexaff
Toshirou Nagai, Tamaki Uehara‐Ichiki, Yukihiro Sawada, Toyozo Sato, Takayuki Aoki, Fukuhiro Yamasaki, Masaru Takeya, Shihomi Uzuhashi, Yuuri Hirooka, Keisuke Tomioka

Bibliographic record

VenueNippon Shokuhin Kagaku Kogaku Kaishi · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicroorganismIsolation (microbiology)The InternetWorld Wide WebBiologyBusinessBacteriaComputer scienceMicrobiology

Abstract

fetched live from OpenAlex

Culture collections (CCs) are organizations that mainly collect strains of microorganisms to stably preserve or maintain them and distribute them to users. There are 23 CCs in Japan ; these are mainly established in universities and national institutes, and contain a total of 411 183 strains of preserved microorganisms (191 692 bacteria, 50 799 yeasts, 49 380 fungi and others), accounting for 20 % of strains preserved in CCs around the world. CCs also store information on their microorganism collections (species, location and origin of isolation, and growth conditions) in databases. These primary data for the identification of strains are open to the public via the Internet, and users can retrieve the data using searching systems offered by the CCs instead of using paper-based catalogs. In CCs, deposited microorganisms are cultured and, depending on the microbial group, freeze-drying or other freezing methods are used to prepare the microorganisms for distribution. At regular intervals, survival tests are carried out to check the quality of the preparations. In addition, CCs offer services other than distribution of their collections, such as e-mail newsletters, workshops on the handling of microorganisms, booklets on the microorganisms, and safety-deposit services for users’ collections.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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 teacher head, not a consensus.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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