How taxonomic revisions affect the interpretation of specimen identification in biological field data
Bibliographic record
Abstract
Cumulative revisions in taxonomy of organisms over time can create difficulties for researchers in numerous scientific fields, including conservation biology. This paper compares the taxonomic names of the Collembola species named in Ivan P. Vtorov’s 1993 paper entitled Feral Pig Removal: Effects on Soil Microarthropods in a Hawaiian Rain Forest, with the more modern Collembola checklists. In comparing Vtorov’s original graphs with recreated graphs, this study finds that out of the sixteen species Vtorov collected, three had changed generic names, two had changed species names, and one was absent from modern Collembola checklists. Five taxa were identified only up to genus, making it impossible to evaluate them in comparison with modern checklists. Only six species matched current moneclature of soil microarthropods. Not only were there taxonomic reclassifications, but also differences in the descriptions of species’ ecological status indicating whether the species was endemic or adventive. None of the three species described as endemic in Vtorov’s study were listed as such in current checklists. Additionally, Vtorov did not deposit voucher specimens, so morphological comparisons or re-identification of species named in his study are impossible. Inconsistencies due to changes in nomenclature and the species’ ecological status or lack of physical documentation can, as shown here, be detrimental to researchers examining data from older studies.
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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 | 0.179 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".