Bibliographic record
Abstract
Index 0-gateway node 42 1-gateway node 42 2D-ABBA 55 2-gateway node 42 3D-ABBA 55 Acceptance-based algorithm 18 Access point (AP) 133 Active selection method 114 Active sensing neighbors discovery 82 Active state modeling 11 Activity scheduling 34, 76 Actuator-actuator coordination 13, 118, 233, 234, 237 Ad hoc on-demand distance vector (AODV) 58, 96, 129 Adaptive demand-driven multicast routing (ADMR) 129 Adaptive flooding 62 Adjustable transmission range Anchor node 217 Angular relaying 115, 116 Anycasting 14, 24, 127, 135, 147-149, 235, 271, 272 Approximation/performance ratio 39 Area dominating set 81 Area-based beaconless broadcasting algorithm (ABBA) 49, 50 Area-based collaborative sleeping (ACOS) protocol 84 Asymptotic optimality 113 Asynchronous protocol 78 Auction aggregate protocol 242 Automated architecture 13 Backbone-based broadcasting 49, 52, 54 Backtracking-based sensor deployment (BTD) 268, 269 Beaconless forwarder planarization (BFP) 115, 116 Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scalable Coordination and Data Communication.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.699 | 0.647 |
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".