Bibliographic record
Abstract
Neutrino astronomy uses large volume detectors to search for astrophysical neutrinos and pinpoint their origin. Detectors such as IceCube at the Geographic South Pole, the Gigaton Volume Detector (GVD) in Lake Baikal and KM3NeT in the Mediterranean sea, instrument up to a cubic kilometer of water or ice for measuring Cherenkov radiation created in neutrino-matter interactions. Especially the utilization of the clear water of the deep sea as Cherenkov medium, in the past, has been facing severe difficulties in deploying and maintaining the offshore infrastructure. Ocean Networks Canada (ONC), an initiative of the University of Victoria, has been operating deep sea infrastructure for scientific purposes, off the Canadian coast since years. One of their network nodes, located on the Pacific abyssal plain, off the coast of Vancouver Island - Cascadia Basin - could be an ideal site for a future neutrino telescope. The Strings for Absorption Length in Water (STRAW) were developed at the Technical University of Munich (TUM) in collaboration with ONC and the University of Alberta. Two strings with optical modules have been deployed at Cascadia Basin in order to measure the optical properties of the water and study the feasibility of a larger installation. We will give a brief overview of the STRAW setup and present first results on the absorption length and optical background at Cascadia Basin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".