Kirby Super Star (SNES) - The Arena 5:16.48 - Mike Yi
Bibliographic record
Abstract
Speed run of The Arena from Kirby Super Star done on April 2 2005. Author's comments: The most thanks should probably go to Quasar84's Damage FAQ on GameFAQs, to provide a good starting point on how to defeat the bosses the quickest. I try to use as efficient an amount of charges as possible to defeat each boss, by balancing the number of Laser and Wave Cannon shots used. Sometimes I use the Spark as well but it works out in the end. To comment on the run, I should say that I got pretty lucky with how the enemy patterns went on me. Since the timer only runs when the boss's life meter is active, I use the rest areas to release and generate the helper as necessary. I do make a few mistakes, enough that I can see a sub 5:10 time as possible. However, I'm quite happy with how this run goes. I'm not sure what my next project will be, or when I'll have the time to do anything (what with the school quarter starting up). But I'm glad I did this, and I now have more respect and appreciation for all the other speed runners on the site than I did before. I don't know if I could keep up such quality work for long periods of time!
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.836 | 0.034 |
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".