Bibliographic record
Abstract
A new indie game called Moonglow Bay was announced at today's Xbox Indie Showcase. While it looks to be mostly calm and serene, and perhaps a bit somber, it seems like something more sinister may be hiding behind the game's adorable surface. On top of other announcements like new titles hitting Xbox Game Pass, Microsoft certainly gave indie games some well-deserved attention with today's Xbox Indie Showcase. The event featured 100 games, with brand new trailers and gameplay for over 25 of them. Some of the other big indie highlights included a Pikmin-like adventure called The Wild at Heart and a puzzle exploration game titled Omno. But alongside these relaxing indie games is Moonglow Bay, set to release sometime this year. Moonglow Bay is a fishing life-sim, where players will start as an amateur fisher on the Eastern Canadian coastline in the 1980s. https://sites.google.com/view/beach-buggy-racing-2-mod-apk https://sites.google.com/view/pocketworld3dmodapkunlimited https://sites.google.com/view/zombie-age-3-mod-apk-unlimited https://sites.google.com/view/rageroadmodapkunlimited2021 https://sites.google.com/view/bazooka-boy-mod-apk-unlimited After the protagonist and their partner moved to Moonglow Bay, cited as being a "town afraid of fishing" in the trailer, a tragedy strikes the couple and now the player must fulfill their partner's last wish by keeping the fishing business afloat. Combining an art style similar to Minecraft's 3D pixelation and slice-of-life gameplay, players will get to go fishing and cook their catch of the day, but will also need to run a fishing shop in Moonglow Bay by selling a variety of recipes, and upgrading the store, gear, and fishing boat. While out in Moonglow Bay, players have the possibility of coming across "epic encounters" and can explore the furthest reaches of the ocean such as frigid glaciers and boiling geysers, the game's description says. And Moonglow Bay itself has its own dark secrets to uncover.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.083 |
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".