Scientific telephone: The cautionary tale of the global coverage of lichens
Bibliographic record
Abstract
Scientific history has many examples of profound statements that are later found to be unsubstantiated. The consequences of such misinformation can be dire. In the present article, we present a case where an unevidenced estimate of global lichen coverage proliferated through both scientific literature and popular media. We traced this estimate to a non-peer-reviewed publication from 1987. We found 76 academic articles (collectively cited 4125 times) and 13 other academic documents citing the statistic, citation chains without source attribution, and instances where the number or context was changed. We also found the statistic 37 times in popular media, which is especially concerning, given that these media communicate science to the broader public. We demonstrate how an unevidenced statement can spread, change through time, and ultimately be repeated without demand for evidence. We hope this case unplugs the telephone and provides a cautionary tale for researchers to ensure critical evaluation of citation and communication practices.
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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 | 0.049 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".