Dissimilar biodiversity data sets yield congruent patterns and inference in lichens
Bibliographic record
Abstract
Large-scale efforts to aggregate and promote the re-use of biodiversity data are leading to novel insights into biogeography and macroecology. However, secondary analyses must account for the tradeoffs and limitations of the original studies. Studies of speciose and taxonomically complex groups often utilize morphospecies or functional subsets as proxies, potentially complicating data re-use. We evaluated whether lichen biodiversity patterns are robust to differences in sampling methodology, utilizing parallel analyses to compare species richness, regional species pool variation, species probabilities of occurrence, and correlation of those three with environmental variables in data sets that cover the same geographic region. Our analyses revealed that, although individual species distributions sometimes differed in idiosyncratic ways, inference based on the aggregated response of multiple species was generally robust across the two datasets, despite differences in observer expertise and functional and taxonomic scope. This suggests that biodiversity data assembled from disparate sources could be used to evaluate biogeographical and macroecological hypotheses in understudied groups such as lichens, particularly at larger spatial scales.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".