Bibliographic record
Abstract
espanolNo es nuestro proposito escribir de la Mesta principalmente, ni analizar en profundidad las sendas o caminos que cruzaban el Senorio en busca de la Canada Conquense para terminar en los Extremos de Castilla, algo de lo que ya se han ocupado otros antes. Nuestra finalidad es muy distinta: pretendemos buscar el origen de la trashumancia en Tierra de Molina hacia tierras andaluzas, analizando el lugar de invernadero de una cabana ganadera, las circunstancias y condiciones del mismo –de las que hay poco escrito o es muy superficial-, prestando especial atencion a la persona del estanciero Pedro Malo de Marcilla, vecino de Molina y morador en Setiles, uno de los mas principales ganaderos de la region, si no el primero. EnglishIt is not our purpose to write the Mesta´s mainly, or analyze in depth the paths or roads that corsed the Senorio of Molina in search of the Canada Conquense to finish at ends of Castile, some of what others are have already o cupied before. Our purpose is very different: we intend to seek the origin of transhumance from land of Molina to Andalusian lands, analyzing a livestock greenhouse place, circumstances and conditions thereof -that there is little written or is very superficial-, paying special attention to the person of the estanciero Pedro Malo de Marcilla, heighbor of Molina, dweller in Setiles, one of most leading farmers in the region, but the first.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".