Bibliographic record
Abstract
Deseo escribir brevemente, con admiracion y gratitud, sobre mi amigo Larry, de 90 anos, un profesor universitario que influyo mucho en el rumbo de mi carrera y al que visite con mi familia hace unos meses durante el verano del hemisferio norte, en su acogedora casa, en el suburbio de Charlotte, en el estado de Vermont, EEUU, vecino a la provincia de Quebec, Canada. Larry sigue siendo un forestal inquieto, con una vitalidad envidiable. Hace unos pocos anos protestaba con un grupo de activistas en Washington DC frente a la Casa Blanca, por la preservacion de unas montanas amenazadas por un oleoducto promovido por el gobierno del presidente Obama. Actualmente es uno de los encargados de velar por el buen estado de los arboles de su comunidad (Town Tree Warden) y esta comprometido en la restauracion de los arboles plantados a lo largo de los caminos de Charlotte. Larry y su esposa Linda inspiran ejemplos de lo mucho que la gente jubilada puede hacer y lo que es una vida dedicada a una causa en la uno cree firmemente.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".