Is Putin Powerful or Just Lucky, Helping Displaced Ukraine Families, How Much Rain Is Enough and Burrowing Owl Conservation
Bibliographic record
Abstract
We begin with a look at the Russian Invasion of Ukraine, specifically, the intentions of President Vladimir Putin and his perceived 'power' on the world stage. We speak with Professor of Sociology Anton Oleinik on why he believes the Russian leader's success has more to do with 'luck' than competency.Next, we continue our conversation on the conflict in Ukraine with a focus on the countless refugees who have fled the war-torn country. We catch up with Orysia Boychuk, President of the Ukrainian Canadian Congress on the steps her organization is taking to welcome displaced citizens to our Province.It's been a tough few years for Alberta's Ag Producers, with dry, hot conditions hampering the efforts of farmers. We take a look at how this year's crops are doing and if the weather is setting up the industry for a better harvest, this year. We speak with Ralph Wright from the Provincial department of Agricultural Meteorology for his thoughts on the season ahead.Finally, it's our monthly chat with Dr. Axel Moehrenschlager from the Wilder/Institute at the Calgary Zoo. This time out, Dr. Moehrenschlager brings us an update on the continuing conservation project focusing on the endangered Burrowing Owl.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".