Geochemical extraction of ceratopsian remains from ironstone
Bibliographic record
Abstract
Ironstone surrounds many fossils and has a hardness that provides a significant challenge to fossil conservators globally. There are various forms of ironstone, with the carbonate forms of siderite and silicates most often containing vertebrate fossils and potentially preserved soft tissues. The ironstone itself is much harder than the fossils preserved within, leading to the mechanical preparation of the fossils––typically using pin vises or airscribes––being time-consuming and presenting the risk of damage. Existing chemical methods for softening ironstone to prepare the fossils have varied success and also pose a risk of damaging the fossil itself. Here we show that carbonic acid can soften ironstone without damaging permineralized bone or potentially preserved soft tissues. Carbonic acid treatments reduced the hardness of ironstone without causing any significant change in hardness, color, chemical composition, or weight to permineralized ceratopsian bones or a modern cervid bone that retained nonmineralized internal tissues. In addition, we found that solutions of sodium hydroxide and hydrogen peroxide were viable for softening ironstone to remove from the permineralized bone when preserved soft tissue recovery is not a priority. The treatments proposed in this study are important as they are applicable to a range of preparation scenarios, are cost-efficient, are relatively safe to handle, and cause no damage to permineralized fossil bones. Putting these methods into practice can lead to more efficient and safe preparation of fossils in ironstone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".