Frequency analysis of maximum annual suspended sediment concentrations in North America / Analyse fréquentielle des maximums annuels de concentration en sédiments en suspension en Amérique du Nord
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Suspended sediments are a natural component of aquatic ecosystems, but when present in high concentrations they can become a threat to aquatic life and can carry large amounts of pollutants. Suspended sediment concentration (SSC) is therefore an important abiotic variable used to quantify water quality and habitat availability for some species of fish and invertebrates. This study is an attempt to quantify and predict annual extreme events of SSC using frequency analysis methods. Time series of daily suspended sediment concentrations in 208 rivers in North America were analysed to provide a large-scale frequency analysis study of annual maximum concentrations. Seasonality and the correlation of discharges and annual peak of suspended sediment concentration were also analysed. Peak concentrations usually occur in spring and summer. A significant correlation between extreme SSC and associated discharge was detected only in half of the stations. Probability distributions were fitted to station data recorded at the stations to estimate the return period for a specific concentration, or the concentration for a given return period. Selection criteria such as the Akaike and Bayesian information criterion were used to select the best statistical distribution in each case. For each selected distribution, the most appropriate parameter estimation method was used. The most commonly used distributions were exponential, lognormal, Weibull and Gamma. These four distributions were used for 90% of stations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it