Bibliographic record
Abstract
The most common objects recovered from excavations were animal bones.These derive from human hunting and fishing activities, from predators bringing prey to the site to be consumed, and from animals that died a natural death at the site.Animal remains from Tse'K'wa consist mainly of bones and teeth from vertebrates, and occasional gastropod shells.Most faunal remains were recovered during excavation when deposits were screened through 3mm hardware cloth.A small number of specimens were obtained when finer screening was used.This was not done systematically, and usually occurred when an excavator noticed concentrations of very small bones and removed a block of sediment and bones for later processing.Most of the analyses conducted on the fauna have used traditional methods of zooarchaeology and paleontology, where the analyst tries to identify the part of the skeleton (e.g.humerus; cervical vertebra) and assign it to some kind of taxonomic designation.For some specimens the analysts could be quite specific, for example when identifying Castor canadensis (beaver) or Vulpes vulpes (red fox).For other specimens the designation might be less specific, such a Mustelidae (weasel family) or Podiceps sp.(undesignated grebe).Unidentifiable specimens were defined as being those specimens for which the analyst could not identify which part of the skeleton was present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.272 | 0.150 |
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".