Detection of temporal trends in transparency across North America using volunteer-collected "snapshot" data
Bibliographic record
Abstract
The Great North American Secchi Dip-In has continuously collected transparency data in North America since 1994. Volunteers collect the data during two weeks spanning Canada Day (July 1) and July 4th. More than 31,000 transparency records have been collected on 6,200 waterbodies. The Dip-In works through existing volunteer monitoring programs, which provides assurance that the data comes from trained volunteers. Transparency values obtained during this “snapshot” event are found to have a low year-to-year within-lake variability (5-6% RSD) and even lower if the data were detrended (3-4% RSD). Using data from waterbodies with five years or more of data, we used a Kendall’s Tau-b to detect temporal trends. Only 54 of 1,362 waterbodies were found to have significant (P = 0.5) decreases in transparency, while 61 had significant increases in transparency. An Ohio volunteer database was used to detect weekly variability throughout the season. The highest weekly variability occurred during the early spring. The second peak (3-4% RSD) occurred in late June, decreasing thereafter into the fall. Year-to-year transparency trends varied throughout the season in slope and significance throughout the season, suggesting that “snapshot” monitoring can provide an estimate of change for a specific seasonal period. The comparability of trends obtained by “snapshot” monitoring and whole-season monitoring will be discussed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".