81 FRAGS in AMERICAN COUNTERATTACK | Hell Let Loose | WW2 50vs50 FPS
Bibliographic record
Abstract
Warning: Content not suitable for under-aged viewing. Link to YouTube version: https://www.youtube.com/watch?v=6WLHEFMILqA Video created by The Shermanator . Rights to the gameplay footage belongs to Team17 Digital Ltd. Livestream: https://www.twitch.tv/shermanator1/ Here is the finale of the 2 HOUR long battle I had on Foy as an American rifleman with my trusty M1 Garand. I had an absolute blast and managed to rack up 81 total frags with an amazing comeback in the very, VERY end of the game! Connect with me: ●Twitch: https://www.twitch.tv/shermanator1/ ●Twitter: http://twitter.com/ShermanatorYT ●Discord: https://discord.gg/kNQrPEd ●Facebook: http://www.facebook.com/ShermanatorYT ●Instagram: https://instagram.com/shermanatoryt/ ●My PC: http://bit.ly/2wqYaNH ●About Me Hi! My name is Samuel, what's up? I am 25 years old and live in Canada (I am Dutch though!). Thanks for checking out my channel. I upload a wide variety of games in 1440p60, including but not limited to Battlefield 5, Men of War (Assault Squad 2), ARMA 3, Red Orchestra 2, Rising Storm 2: Vietnam, The Wargame Series, SQUAD, Post Scriptum, Heroes and Generals & Company of Heroes! I try to maintain a healthy balance between fun and tactical gameplay, mixing videos with tips, tricks and random gameplay that can be from any game! If you like the content make sure to hit the subscribe button! Want to contact me? Send me an Email or tweet me, I rarely check YouTube's private messages! ~Thanks for watching!
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".