Data for: Gas Transfer Velocities Evaluated Using Carbon Dioxide as a Tracer Show High Streamflow to Be a Major Driver of Total CO2 Evasion Flux for a Headwater Stream
Bibliographic record
Abstract
This dataset contains direct in-situ measurements and modeled values of stream properties and air temperature of a steep, turbulent headwater stream in southwestern British Columbia, Canada, between November 2016 and June 2017. The dataset is associated with a study of CO2 evasion from experimental stream G-H in the University of British Columbia Malcolm Knapp Research Forest, which estimated gas transfer velocities of CO2 (kCO2) and CO2 evasion using an automated in situ CO2 tracer technique. McDowellJohnson2018_continuousdata.tab contains continuous half-hourly stream data for the duration of the study period. McDowellJohnson2018_injectiondata.tab contains averaged values of stream data associated with 38 one-hour injections of CO2. Continuous data variables: date-time, CO2 concentration, pH, water temperature, electrical conductivity, air temperature, stream discharge, stream depth, water velocity, modeled gas transfer velocity of CO2, modeled gas transfer velocity of CO2 normalized to a Schmidt number of 600, dissolved oxygen concentration Injection data variables: date-time, stream depth, gas transfer velocity of CO2, gas transfer velocity of CO2 normalized to a Schmidt number of 600, stream discharge, water temperature, water velocity This work was conducted on the unceded, ancestral territories of the xʷməθkʷəy̓əm (Musqueam) and Katzie peoples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.013 | 0.016 |
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".