Bibliographic record
Abstract
In wireless sensor networks, we usually need to detect interesting events based on the information gathered from multiple sensors. One useful detection is to test whether or not the average sensory value within an area is larger than a given threshold. Such type of query is called geo-range query. It should report the geographic centers where the average value of nearby sensors is greater than a certain threshold. Answering geo-range query is nontrivial because we do not know in advance the satisfying geographic centers, which may not be necessarily the same as the locations of sensors. We develop two efficient algorithms: the brute-force search algorithm and the sweep-line algorithm. The brute-force search algorithm uses exhaustive search to enumerate all possible satisfying sub-regions. Its time complexity is O(n3), where n is the number of sensor nodes. The sweep-line algorithm uses a virtual line sweeping top-down through the plane. The algorithm takes O(n2log n) running time, and still obtains exact solution to the problem.
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 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".