Comment on the letter of the Society of Vertebrate Paleontology (SVP) dated April 21, 2020 regarding "Fossils from conflict zones and reproducibility of fossil-based scientific data": Myanmar amber
Bibliographic record
Abstract
Motivation for this comment Recently, the Society of Vertebrate Paleontology (SVP) has sent around a letter, dated 21st April, 2020 to more than 300 palaeontological journals, signed by the President, Vice President and a former President of the society (Rayfield et al. 2020). The signatories of this letter request significant changes to the common practices in palaeontology. With our present, multi-authored comment, we aim to argue why these suggestions will not lead to improvement of both practice and ethics of palaeontological research but, conversely, hamper its further development. Although we disagree with most contents of the SVP letter, we appreciate this initiative to discuss scientific practices and the underlying ethics. Here, we consider different aspects of the suggestions by Rayfield et al. (2020) in which we see weaknesses and dangers. It is our intent to compile views from many different fields of palaeontology, as our discipline is (and should remain) pluralistic. This contribution deals with the aspects concerning Myanmar amber. Reference is made to Haug et al. (2020a) for another comment on aspects concerning amateur palaeontologists/ citizen scientists/private collectors.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".