Lichenometric Dating: Science or Pseudo-Science?
Bibliographic record
Abstract
Abstract The popular technique of estimating ages of deposits from sizes of lichens continues despite valid criticism, and without agreement on range of utility, treatment of error, and methods of measurement, sampling, and data handling. A major source of error is the assumption that the largest lichen(s) colonized soon after deposition and will survive indefinitely. Recent studies on lichen mortality suggest that this assumption is untenable. Meanwhile, the use of “growth curves” constructed from independently dated substrates is problematic for many reasons, but this has not prevented the publication of baseless claims of accuracy and ages that are extrapolated well beyond data. Experiments indicate that numeric lichenometric ages are not reliable, and in general do not advance the cause of Quaternary science. There are a few studies suggesting reliability, and indeed there may be cases where lichens and growth curves actually provide realistic numerical ages. But it cannot be foretold which lichen assemblages will provide good ages and which bad ages. The logical conclusion is that no assumption of good ages can be made, and that it is folly to assign numerical ages to a deposit on the basis of lichen sizes.
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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 | 0.029 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".