Looking back on a decade of barcoding crustaceans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Species identification represents a pivotal component for large-scale biodiversity studies and conservation planning but represents a challenge for many taxa when using morphological traits only. Consequently, alternative identification methods based on molecular markers have been proposed. In this context, DNA barcoding has become a popular and accepted method for the identification of unknown animals across all life stages by comparison to a reference library. In this review we examine the progress of barcoding studies for the Crustacea using the Web of Science data base from 2003 to 2014. All references were classified in terms of taxonomy covered, subject area (identification/library, genetic variability, species descriptions, phylogenetics, methods, pseudogenes/numts), habitat, geographical area, authors, journals, citations, and the use of the Barcode of Life Data Systems (BOLD). Our analysis revealed a total number of 164 barcoding studies for crustaceans with a preference for malacostracan crustaceans, in particular Decapoda, and for building reference libraries in order to identify organisms. So far, BOLD did not establish itself as a popular informatics platform among carcinologists although it offers many advantages for standardized data storage, analyses and publication.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it