Dissolved inorganic carbon (DIC), total alkalinity, water temperature, salinity, dissolved oxygen, nutrients and other hydrographic and chemical data collected from discrete samples and profile observations during the CCGS Louis S. St-Laurent Joint Ocean Ice Study (JOIS-16) cruise (EXPOCODE 18SN20160922) in the Arctic Ocean, Beaufort Sea from 2016-09-22 to 2016-10-18 (NCEI Accession 0232552)
Bibliographic record
Abstract
This dataset includes discrete profile measurements of dissolved inorganic carbon (DIC), total alkalinity, water temperature, salinity, dissolved oxygen, nutrients and other hydrographic and chemical data collected from discrete samples and profile observations during the CCGS Louis S. St-Laurent Joint Ocean Ice Study (JOIS-16) cruise (EXPOCODE 18SN20160922) in the Arctic Ocean, Beaufort Sea from 2016-09-22 to 2016-10-18. The Joint Ocean Ice Study (JOIS) in 2014 is an important contribution from Fisheries and Oceans Canada to international Arctic climate research programs. Primarily, it involves the collaboration of Fisheries and Oceans Canada researchers with colleagues in the USA from Woods Hole Oceanographic Institution (WHOI). The scientists from WHOI lead the Beaufort Gyre Exploration Project (BGEP) and the Beaufort Gyre Observing System (BGOS) which forms part of the Arctic Observing Network (AON).
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".