</title> </titles> <publication_date> <year>0</year> </publication_date> <pages> <first_page></first_page> <last_page></last_page> </pages> <publisher_item> <item_number item_number_type='arNumber'></item_number> </publisher_item> <doi_data> <doi>10.1109/LCOM.2007.357451</doi> <resource>http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber= </resource> </doi_data> </journal_article> <journal_article> <titles> <title><![CDATA[</title> </titles> <publication_date> <year>0</year> </publication_date> <pages> <first_page></first_page> <last_page></last_page> </pages> <publisher_item> <item_number item_number_type='arNumber'></item_number> </publisher_item> <doi_data> <doi> </doi> <resource>http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber= </resource> </doi_data> </journal_article> <journal_article> <titles> <title><![CDATA[</title> </titles> <publication_date> </publication_date> <pages> <first_page></first_page> <last_page></last_page> </pages> <publisher_item> <item_number item_number_type='arNumber'></item_number> </publisher_item> <doi_data> <doi> </doi> <resource>http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber= </resource> </doi_data> </journal_article> <journal_article> <titles> <title><![CDATA[Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing
Bibliographic record
Abstract
Collaborative spectrum sensing enables opportunistic unlicensed access to the unused portions of the licensed spectrum. We characterize the performance degradation of collaborative sensing due to correlated shadowing by deriving a lower-bound on the probability of missing the opportunities for unlicensed access. Moreover, we evaluate the effective number of collaborating users in terms of the distance spread of the sensing network and the characteristics of the propagation environment. This has practical implications in terms of protocol design as having a few number of users collaborate over a large distance may be more effective than a dense sensing network confined to a small area
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.800 | 0.849 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".