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Record W2563980006 · doi:10.1139/as-2016-0050

Biodiversity databases in Russia: towards a national portal

2016· article· en· W2563980006 on OpenAlexvenueno aff
Natalya Ivanova, Maxim Shashkov

Bibliographic record

VenueArctic Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersDivision of Arctic SciencesLobachevsky State University of Nizhny NovgorodTomsk State University
KeywordsDatabaseUploadBiodiversityInterface (matter)Computer scienceWorld Wide WebData administrationGeographyDatabase designEcologyDatabase schema

Abstract

fetched live from OpenAlex

Russia holds massive biodiversity data accumulated in botanical and zoological collections, literature publications, annual reports of natural reserves, nature conservation, and monitoring study project reports. While some data have been digitized and organized in databases or spreadsheets, most of the biodiversity data in Russia remain dormant and digitally inaccessible. Concepts of open access to research data is spreading, and the lack of data publishing tradition and of use of data standards remain prominent. A national biodiversity information system is lacking and most of the biodiversity data are not available or the available data are not consolidated. As a result, Russian biodiversity data remain fragmented and inaccessible for researchers. The majority of Russian biodiversity databases do not have web interfaces and are accessible only to a limited numbers of researchers. The main reason for lack of access to these resources relates to the fact that the databases have previously been developed only as a local resource. In addition, many sources have previously been developed in the desktop database environments mainly using MS Access and, in some cases, earlier DBMS for DOS, i.e., file-server system, which does not have the functionality to create access to records through a web interface. Among the databases with a web interface, a few information systems have interactive maps with the species occurrence data and systems allowing registered users to upload data. It is important to note that the conceptual structures of these databases were created without taking into account modern standards of the Darwin Core; furthermore, some data sources were developed prior to the first work version of the Darwin Core release in 2001. Despite the complexity and size of the biodiversity data landscape in Russia, the interest in publishing data through international biodiversity portals is increasing among Russian researchers. Since 2014, institutional data publishers in Russia have published about 140 000 species occurrences through gbif.org. The increase in data publishing activity calls for the creation of a GBIF node in Russia, aiming to support Russian biodiversity experts in international data work.

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)0.013
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0020.001
Scholarly communication0.0120.019
Open science0.0030.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.010

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 head0.054
GPT teacher head0.280
Teacher spread0.226 · 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.

Study designNot applicable
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

Citations17
Published2016
Admission routes1
Has abstractyes

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