Using the Geology of Your Neighbourhood and City for Geoscience Outreach
Bibliographic record
Abstract
Most Canadians live, work and attend school in urban areas. Your neighbour-hood has wonders such as snow-bank stratigraphy, sidewalk sedimentology, and building stone. Urban fieldtrips require little money and little time to run and they can inspire participants. An inner-city school teacher confirmed my belief in urban fieldtrips when she said, “I’ve never been able to take my students anywhere but we can now take a city bus downtown and see the world. Thank You!” So open your eyes to the grand world of geology around you. Take others on a neighbourhood trip; you will inspire, motivate and educate them. SOMMAIRE La majorite des Canadiens vivent, travaillent et etudient en milieu urbain. Votre voisinage meme offre des occasions d’emerveillement comme des exemples de stratigraphie de bancs de neige, de sedimentologie des trottoirs, et de pierres de construction. Les excursions urbaines sont peu couteuses, prennent peu de temps et peu-vent s’averer stimulantes pour leurs participants. Une enseignante d’une ecole en milieu urbain a confirme mes idees sur les excursions urbaines lorsqu’elle a declare, « Je n’avais jamais pu sortir mes eleves, mais maintenant, nous pouvons prendre l’autobus et voir le monde, grâce a vous. » Soyez donc a l’affut de l’univers geologique qui pointe dans votre milieu. Amener les gens de votre entourage en excursion; vous les aurez interesses, motives et eduques.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".