INFLUENCIA IN VITRO DE LAS CARACTER?STICAS DE LAS MORDEDURAS CANINAS EN LA IDENTIFICACI?N DEL PERRO AGRESOR DE RAZAS PASTOR ALEM?N, PASTOR BELGA, ROTTWEILER Y LABRADOR. LOCAL DE POLIC?A CANINA DISTRITO DE PAUCARPATA. AREQUIPA 2013
Bibliographic record
Abstract
MORDEDURA CONCEPTO GENERAL MORDEDURA IN VITRO CLASES DE MORDEDURA TIPOS DE MORDEDURA LESIONES O HERIDAS POR MORDEDURA HUELLAS DE MORDEDURA CARACTER?STICAS DE LAS HUELLAS DE MORDEDURA CARACTER?STICAS DE LAS LESIONES POR MORDEDURA DE PERROS REGISTRO DE LAS MORDEDURAS ESTUDIO DE LAS HUELLAS DE MORDEDURA IDENTIFICACI?N DENTAL CONCEPTO GENERAL IMPORTANCIA DE LA IDENTIFICACI?N IMPORTANCIA DE LA IDENTIFICACI?N POR HUELLAS DE MORDEDURA EL PERRO CONCEPTO GENERAL RAZAS DE PERRO ANATOM?A DE LAS PIEZAS DENTARIAS CANINAS MORFOLOG?A DENTARIA CANINA NOMENCLATURA
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".