Bibliographic record
Abstract
Update from KLRSAt the Kluane Lake Research Station (KLRS), 2017 was a busy year of upgrades and improvements to bring the station into alignment with the policies and practices of our home institution, the Arctic Institute of North America, University of Calgary.These improvements included firesmarting, bringing the kitchen up to commercial code, distribution of safety equipment, and refurbishment of all stoves and chimneys to meet safety codes.Oil-drip and propane stoves have been replaced with wood stoves using locally sourced wood, in pursuit of our goal to wean the station off fossil fuels.Two new high-speed satellite dishes were added to double the Wi-Fi capability, and new solar and battery systems were installed to support year-round Internet and phone availability.AINA has continued to meet its research and education mandate through KLRS, hosting a rich array of station users in summer 2017.We were successful in securing funding for 2017-19 from the Natural Sciences and Engineering Research Council of Canada under the Operations and Maintenance Support program.Highlights from the summer research and education programs include hosting a large group from the University of Exeter (the first international university field school at KLRS) and an exceptional Artist in Residence (Cedra Wood), research visits from the Universities of Turku (Finland) and Copenhagen (Denmark) through the INTERACT transnational access program, and hosting a BioBlitz Canada event in June.KLRS has now been formally approved as a CryoNet station within the World Meteorological Organization (WMO) Global Cryosphere Watch (GCW) initiative.We join Eureka, Nunavut, as the second Canadian GCW site.We were awarded funds from Polar Knowledge Canada to support the GCW implementation at KLRS, in partnership with Simon Fraser University, the University of Ottawa, and Yukon College/Yukon Research Centre.The KLRS weather station was upgraded with the addition of a four-component radiometer and satellite data transmission.A Geonor precipitation gauge will be installed in spring 2018.As one of only two pilot sites in the world, KLRS was selected by the WMO to test GCW practices for North American sites.Please get in touch with us at KLRS@ucalgary.ca with any requests or suggestions for next season.Check online at arctic.ucalgary.cafor updates and at http://arctic.ucalgary.ca/make-klrs-reservation to book your stay.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.296 | 0.219 |
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".