Beyond skepticism: uncovering cryptic refugia using multiple lines of evidence
Bibliographic record
Abstract
The increasingly popular opinion that temperate plant and animal species persisted in many small refugia in Europe north of the main southern peninsular refugia directly challenges a classical paradigm. As indirect evidence for the existence of such glacial microrefugia readily accumulates, it is interesting to note that the few direct in situ occurrences of fossils testifying their local existence are almost always debated. Our recent study on European beech (Fagus sylvatica) refugia in south-western France was no exception to this trend. Yet, as mentioned by the philosopher Bertrand Russell, it is not possible ‘to get anywhere if we start from skepticism. We must start from a broad acceptance of whatever seems to be knowledge and is not rejected for some specific reason.’ We therefore believe that no a priori preference should be given to one of the two scenarios of migration from a distant macro-refugium or of survival in situ in small refugia. Microrefugia are by definition spatially restricted and include few and possibly less fit individuals, so that a direct assessment for their existence at the stand-scale is extremely challenging. In fact, without prior information on the possible location of these refugia, finding fossils testifying to their existence is akin to looking for a needle in a haystack. We believe that to uncover glacial micro-refugia, the fossil record needs not to be abundant, as long as the claims are reasonable and the dataset includes both indirect and robust direct field evidence for local presence during the most severe climatic conditions. The criticisms of Huntley extend beyond our own study to other recently published studies on so-called cryptic glacial refugia, that is putative microrefugia inferred indirectly from genetic analyses of extant populations, species distribution modeling (SDM) or regional-scale pollen analyses. As we will now show, considerable progress has been made recently in these fields that deserve to be more widely appreciated.
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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 | 0.119 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".