Bibliographic record
Abstract
LA UNAM, MEDIANTE LOS INSTITUTOS DE CIENCIAS NUCLEARES Y DE INGENIERIA, PARTICIPA EN EL EXPERIMENTO DAMIC (DARK MATTER IN CCDS), CUYO PROPOSITO ES LA BUSQUEDA DIRECTA DE MATERIA OSCURA. EL DETECTOR UTILIZA DISPOSITIVOS DE CARGA ACOPLADA O CCD (CHARGED COUPLED DEVICES) DE TIPO CIENTIFICO Y ES INSTALADO A DOS MIL METROS DE PROFUNDIDAD, EN EL LABORATORIO SUBTERRANEO SNOLAB, EN CANADA. LA MAYOR PARTE DE LA MATERIA DEL UNIVERSO NO PUEDE VERSE Y NI SIQUIERA SE CONOCE DE QUE ESTA COMPUESTA; LA PRESENCIA DE LA OSCURA HA SIDO INFERIDA SOLO POR MEDIO DE SUS EFECTOS GRAVITACIONALES. DETERMINAR SU NATURALEZA CONSTITUYE UNO DE LOS PROBLEMAS MAS SERIOS DE LA FISICA Y LA ASTROFISICA CONTEMPORANEAS. ALEXIS AGUILAR Y JUAN CARLOS D'OLIVO, AMBOS DE CIENCIAS NUCLEARES, ENCABEZAN AL GRUPO DE UNIVERSITARIOS QUE COLABORA EN EL EQUIPO INTERNACIONAL DE CIENTIFICOS QUE, CON ESE DETECTOR EXTRAORDINARIAMENTE SENSIBLE, INTENTARA DETERMINAR, A PARTIR DEL PRIMER CUATRIMESTRE DE 2015, SI HAY UNA INTERACCION, AUNQUE SEA MUY DEBIL, ENTRE LA MATERIA OSCURA Y LA ORDINARIA. EN EL TRANSCURSO DE 2016 SE TENDRAN LOS PRIMEROS RESULTADOS Y SE ESTARA MAS CERCA DE SABER QUE ES, PREGUNTA QUE INQUIETA A LOS CIENTIFICOS Y QUE HOY EN DIA ES UNA PRIORIDAD DE LA FISICA DE PARTICULAS, LA ASTROFISICA Y LA COSMOLOGIA. AL PLATICAR CON LA REPORTERA DE GACETA UNAM (LAURA ROMERO), AGUILAR Y D'OLIVO AHONDAN EN LAS INVESTIGACIONES EFECTUADAS SOBRE LA MATERIA OSCURA Y DETALLAN LA PARTICIPACION DE LOS ESPECIALISTAS UNIVERSITARIOS EN EL DESARROLLO DE ESTE PROYECTO INTERNACIONAL.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".