A 30-year dataset of CO2 in flowing freshwaters in the United States
Bibliographic record
Abstract
Abstract Increasing atmospheric carbon dioxide (CO2) concentrations have been linked to effects in a wide range of ecosystems and organisms, with negative effects of elevated CO2 documented for marine organisms. Less is known about the dynamics of CO2 in freshwaters, but the potential exists for freshwater organisms to be challenged by elevated CO2. In flowing freshwaters CO2 exhibits more variability than in lakes or the ocean, yet spatiotemporally extensive direct measures of CO2 in freshwater are rare. However, CO2 can be estimated from pH, temperature, and alkalinity—commonly collected water quality metrics. We used data from the National Water Quality Monitoring Council along with the program PHREEQC to estimate CO2 in flowing freshwaters across 35,000 sites spanning the lower 48 US states from 1990 through 2020. Site data for water chemistry measurements were spatially joined with the National Hydrology Dataset. Our resulting dataset, CDFLOW, presents an opportunity for researchers to add CO2 to their datasets for further investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".