Design of monitoring networks using <i>k</i>‐determinantal point processes
Bibliographic record
Abstract
In this paper, we introduce a design strategy based on k‐determinantal point processes (k‐DPPs). The k‐DPP design is a flexible design scheme that is able to yield spatially balanced designs while also imposing diversity of the selected locations based on extra sources of information known to be related to the underlying process of interest. The methodology is able to handle both the designing and the redesigning of a monitoring network. In particular, we discuss how a k‐DPP design can be used as a randomized alternative for the space‐filling designs when the objective is to provide a good spatial coverage of the region of interest. Furthermore, we discuss how the k‐DPP optimal design objective is remarkably similar to that of entropy design for Gaussian fields. Because the optimization for entropy designs is a NP‐hard problem, we explore an approximate solution based on a k‐DPP sampling design strategy. Through a case study of augmentation of a network for monitoring temperatures, we illustrate how a k‐DPP sampling design strategy can yield an approximation for the entropy solution.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".