How to carve out a life in hockey? Listen to Brandon Wong's life story with Complete Sports Media
Bibliographic record
Abstract
#BCHL #Canucks #NCAA #ECHL #AHL #KHL This is a shorter version of the life story of Brandon Wong. As a lot of you know this is one of the nicest people in hockey and had a lot of stops and has made so many great connections. With a career that started in minor hockey in BC, Canada then into junior and the BCHL off to college and then to pro hockey. He has played for and represented 21 organizations very well and he has had a very adventurous journey. He played over 400 professional games over 9 seasons in over 8 different countries. We had an amazing and lengthy conversation with him and posted the full version the other day. This episode is just focusing on some of the highlights on his junior, college and pro stops. Please watch the full version eventually but for those of you pressed for time or with ADD this could present you some of the picture. Enjoy and let us and Brandon know how much you enjoyed the discussion. Please go to our website www.completesportsmedia.com for more podcast episodes and tons of details on our organization. Also go to www.brandonwonghockey.com to get many details on his life and his coaching, training and mentoring business he has recently embarked on! Take Care, Love yah, Bye for now!
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.214 | 0.019 |
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; both teacher heads agree on what is shown here.
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".