A temperá presenza militar romana (séculos II a.C. - I d.C.)
Bibliographic record
Abstract
[GA]Apresenza militar romana de época temperá (séculos II a.C. - I d.C.) na actual provincia de Ourense era ata hai pouco practicamente descoñecida. Os estudos sobre o exército romano nesta zona concentrábanse fundamentalmente no forte romano de Aquis Querquennis (Bande, Ourense), cuxa ocupación se data en torno ao último cuarto do século I d. C. e inicios do II d. C. (Rodríguez Colmenero & Ferrer Sierra 2006; Puente et al. 2018), nunha cronoloxía moi posterior á conquista romana do territorio galaico. Nos últimos anos, grazas ao incremento da dixitalización da arqueoloxía coa introdución de novas técnicas de teledetección, desde o noso colectivo de investigación, Romanarmy.eu, puidemos localizar un conxunto de novos sitios militares romanos de carácter temporal na provincia de Ourense (Figura 1) (Costa-García et al. 2017, 2019). Estes sitios caracterízanse por unha materialidade de escasa entidade e pola pouca expresión das súas estruturas sobre o terreo (Peralta Labrador 2002). Deste xeito, só foi posible identificalos a través do uso das técnicas de teledetección de recente desenvolvemento, coma o LiDAR aéreo (Costa-García & Fonte 2017), que permite recoñecer o terreo baixo as densas cubertas vexetais do Noroeste. De forma xenérica, estes recintos cobren un abanico temporal entre o século II a.C. e inicios do século I d.C., un período histórico para o cal apenas dispoñiamos de datos arqueolóxicos sobre a presenza militar romana
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".