The Most Likely Object to be Seen Through a Window
Bibliographic record
Abstract
We study data structures to answer window queries using stochastic input sequences. The first problem is the most likely maximal point in a query window: Let [Formula: see text] be constants, with [Formula: see text]. Let [Formula: see text] be a set of [Formula: see text] points in [Formula: see text], for some fixed [Formula: see text]. For [Formula: see text], each point in [Formula: see text] is associated with a probability [Formula: see text] of existence. A point [Formula: see text] in [Formula: see text] is on the maximal layer of [Formula: see text] if there is no other point [Formula: see text] in [Formula: see text] such that [Formula: see text]. Consider a random subset of [Formula: see text] obtained by including, for [Formula: see text], each point of [Formula: see text] independently with probability [Formula: see text]. For a query interval [Formula: see text], with [Formula: see text], we report the point in [Formula: see text] that has the highest probability to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. We solve a special problem as follows. A sequence [Formula: see text] of [Formula: see text] points in [Formula: see text] is given ([Formula: see text]), where each point [Formula: see text] has a probability [Formula: see text] of existence associated with it. Given a query interval [Formula: see text] and an integer [Formula: see text] with [Formula: see text], we report the probability of [Formula: see text] to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. The second problem we consider is the most likely common element problem. Let [Formula: see text] be the universe. Let [Formula: see text] be a sequence of random subsets of [Formula: see text] such that for [Formula: see text] and [Formula: see text], element [Formula: see text] is added to [Formula: see text] with probability [Formula: see text] (independently of other choices). Let [Formula: see text] be a fixed real number with [Formula: see text]. For query indices [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], with [Formula: see text] and [Formula: see text], we decide whether there exists an element [Formula: see text] with [Formula: see text] such that [Formula: see text] in [Formula: see text] time using [Formula: see text] space and report these elements in [Formula: see text] time, where [Formula: see text] is the size of the output.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".