Lichenometric Dating: Science or Pseudo-Science?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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 it