Discrete profile measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), water temperature, salinity, nutrients and dissolved oxygen during R/V John Strickland Properties Survey cruises 2015-07, 2015-11, 2016-07 and 2017-01 (EXPOCODEs 18S720150722, 18S720151109, 18S720160726 and 18S720170124) in the Juan de Fuca Strait of the North American Pacific coast from 2015-07-22 to 2017-01-24 (NCEI Accession 0232540)
Bibliographic record
Abstract
This dataset consists of discrete profile measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), water temperature, salinity, nutrients and dissolved oxygen during R/V John Strickland Properties Survey cruises 2015-07, 2015-11, 2016-07 and 2017-01 (EXPOCODEs 18S720150722, 18S720151109, 18S720160726 and 18S720170124) in the Juan de Fuca Strait of the North American Pacific coast from 2015-07-22 to 2017-01-24. There were 58 DIC/TA pairs collected from Juan de Fuca Strait near to the sewage outfalls off the coast of Victoria BC, with 27% replication rate and pooled standard deviations of 0.66 and 1.28 umol/kg and respectively. Discrete total alkalinity (TA), dissolved inorganic carbon (DIC), and nutrient samples were taken from rosette casts with 10L Niskin bottles and analyzed at the Institute of Ocean Sciences. CTD profiles were recorded with each rosette cast. Data interpreted and published in Krogh et al. 2018 – Risks of hypoxia and acidification in the high energy coastal environment near Victoria Canadas untreated municipal sewage outfalls.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".