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Bibliometric Analysis of Poppy Alkaloids and Morphine Biosynthesis

2025· article· en· W4414092465 on OpenAlexaboutno aff
Tahsin Beycioğlu

Bibliographic record

VenueBlack Sea Journal of Agriculture · 2025
Typearticle
Languageen
FieldMedicine
TopicBerberine and alkaloids research
Canadian institutionsnot available
Fundersnot available
KeywordsPoppyThematic analysisCitationBibliometricsWeb of scienceOpium Poppy

Abstract

fetched live from OpenAlex

This bibliometric analysis examines the scientific literature development in poppy alkaloids and morphine biosynthesis from 1980 to 2024. Using data from 845 Web of Science publications, we conducted comprehensive analyses including annual scientific production, international collaboration networks, keyword analysis, citation patterns, and thematic mapping. Our findings reveal steady growth in scientific output since the 1980s, peaking at 45 publications in 2016. The USA, Canada, UK, Germany, and Australia emerge as the most productive and influential countries in international collaboration networks. Citation analysis identifies Facchini (1996), Morishige (2000), Liscombe (2007), and Hagel (2010) as the field's most influential reference points. Thematic mapping identifies "opium poppy," "molecular-cloning," and "expression" as motor themes driving the field, while "biosynthesis," "alkaloids," and "morphine" constitute core themes. Word cloud analysis shows "Papaver somniferum" as the most frequently used term (frequency: 76), representing 8% of the total research focus according to treemap analysis. This study provides a comprehensive mapping of the scientific structure and developmental dynamics in poppy alkaloids and morphine biosynthesis research, offering valuable insights for identifying research gaps and predicting future directions in this interdisciplinary field.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1340.178
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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
Published2025
Admission routes1
Has abstractyes

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