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Record W1967304706 · doi:10.1080/11035897.2014.880070

Geobiodiversity Database (GBDB) in stratigraphic, palaeontological and palaeogeographic research: graptolites as an example

2014· article· en· W1967304706 on OpenAlexaff
Junxuan Fan, Xudong Hou, Qing Chen, Michael J. Melchin, Daniel Goldman, Linna Zhang, Zhongyang Chen

Bibliographic record

VenueGFF · 2014
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsContext (archaeology)PaleontologyHistorical geologyGeologyStratigraphyDatabaseData scienceEarth scienceComputer scienceSedimentology

Abstract

fetched live from OpenAlex

The Geobiodiversity Database (GBDB) Project is dedicated to the construction and maintenance of a web-enabled taxonomic, stratigraphic and geographic database for information gathered from the fossil record. Its goal is to facilitate regional and global scientific collaborations focused on studying the history, diversity, geography and environmental context of life on Earth. After 7 years of development, its abundant data resources and diverse integrated functions make it a useful platform for stratigraphic, palaeontological and palaeogeographic analysis. Graptolites are the first major fossil group that has been the focus of our efforts to compile, standardize, digitize and analyse over the past several years. Here, we use the graptolite group as an example to introduce the major data resources and functionality of the GBDB.

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)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.016
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.011

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 head0.102
GPT teacher head0.293
Teacher spread0.191 · 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
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

Citations18
Published2014
Admission routes1
Has abstractyes

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