Bibliographic record
Abstract
The origin of fossils After an animal dies, its behavior immediately stops. Of course! Thus, behavior is the first component of the phenotype to be lost. After this, DNA and soft tissues are also rapidly lost. Large and small animals may eat or scavenge the carcass, dismembering the body, stripping away flesh, and breaking open bones that are rich in marrow. Bacteria and fungi alter soft tissues as decay takes place. Nevertheless, soft-bodied organisms may be preserved as flattened carbon films, preserved as calcium phosphate, or altered by early mineralization. For example, in the 425 mya Eramosa Formation of Canada, animal tissues containing melanin were altered by sulfur early after death; this caused resistance to bacterial decay (von Bitter et al ., 2007). Exceptionally well-preserved material from this formation disproved an idea that shallow marine fossils after the Cambrian would be unlikely to fossilize in great detail. It had previously been thought that an increase in burrowing organisms after the Cambrian would irretrievably alter sediments. Specimens from the Eramosa Formation show that this is not necessarily the case. However, it must be understood that the likelihood of any single ancient organism being preserved is miniscule. It is only the multitude of organisms living over vast reaches of geological time that allows these faint probabilities to emerge as recognizable fossils. A special sub-discipline of paleontology has been created to study all of the processes that affect an organism immediately after death until its discovery as a fossil. This is taphonomy. Techniques used by paleontologists to study taphonomy and taphonomic processes affecting fossils are also used by archaeologists when analyzing archaeological materials and sites. They are also used by forensic anthropologists when analyzing material from a crime scene, especially when considering events around the time of death and after death (Klepinger, 2006).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".