Assessing taxon names in palynology (I): working with databases
Bibliographic record
Abstract
An overview of the history of a taxon name and its current status are critical in taxonomy; and selecting the correct name from among synonyms is commonly important in applied studies. This often onerous task can be facilitated by working with databases that can be used to develop an overview of the number of species within a genus as well as their spatial and temporal distributions and their frequency of use. For example, a quantitative analysis of the use of competing names can inform formal proposals to conserve, protect, or reject names. Currently, palynologists can consult two extensive databases, Palynodata and the John Williams Index of Palaeopalynology, both of which were discontinued, in 2006 and 2015, respectively. As new data accumulates, analyses require augmentation from uncurated online resources such as Google Scholar. Here, we conducted a case study for four Mesozoic genera relevant for example in studying the Triassic–Jurassic transition in the Germanic Basin. The genera contain a total of 65 species. The study compared the output from the two databases of references and an online source for the species inventory over time by analysing more than 2000 citations and their cross-occurrences. We found that the John Williams Index is the most accurate and extensive, but it can only be consulted in person in London. Palynodata, available as a dataset or online, is the more accessible source of information. Our study also shows that no significant difference results from whether one combines the John Williams Index or Palynodata with Google Scholar since using any two of these sources provide a recovery of at least 75% of all citations compared to using all three. In conclusion, each database has its own advantages and disadvantages, and when working under time pressure, the choice of database depends on the research question asked.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.041 | 0.079 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".