Technology trickle down in BC The Olympic Games have come and gone in
Bibliographic record
Abstract
Vancouver and British Columbia is still giddy with the astounding success of our Canadian athletes. The events and the athleticisms shown were nothing short of remarkable, and athletes that didn’t step up to the podium still performed blazingly well. Consider the 50 km biathlon; the convergence of physical elitism where the separation between first place and fourth was a mere 1.5 seconds after more than two hours of racing, a difference of less than two per mil. At the same time the technology associated with the games was breathtaking. Apparently more people accessed the games than ever before via conventional means, but also over mobile media in a way that will likely change how games are broadcast and accessed from this point forth. I find myself constantly amazed by technological advances in all walks of life, including geoscience. The European based Maxwave project for example, which has documented the presence of rogue waves using satellite borne radar covering large swaths of ocean. The second, hydrologic models are arguably more like the mystery whereby they become potentially more revealing, but also harder to use and interpret with every new complex algorithm. The second article considers the applicability of several hydrologic models to forest management issues in BC that will hopefully bring users closer to a usable understanding of those models. In either case, as technology trickles down into the hands of users, it behooves us to take notice. I hope these articles help somewhat. As always, Island Geoscience welcomes new submissions or ideas for articles. If there are topics you would like to see covered, or if you have an article or idea you would like to contribute, please send me your ideas at:
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".