Calculation of SS, TN and TP Specific Concentration Factors for Land-Use Types Using a Simple Watershed Model
Bibliographic record
Abstract
To contribute to the prediction of rainfall-related disasters, specific concentration factors that indicate the suspended solid (SS), total nitrogen (TN), and total phosphorus (TP) load intensities to river water for each land-use type were calculated using a simple watershed land-use model across Japan by applying the following multiple regression equation, according to the land-use ratios and published SS, TN and TP data. C=i=14aixi C: SS, TN and TP concentrations (mg L-1); ai: SS, TN and TP specific concentration factor for land use i; xi: ratio of land use i; land use: 1 paddy fields, 2 upland fields, 3 forests, 4 urban areas. The land-use ratios for watersheds, whose lower ends were observation points of river water quality, were determined by the GIS technique using a published database of DEM and LULC mesh data. The SS specific concentration factor was 15.4 (from a 95% lower limit value of 12.0 to a 95% upper limit value of 18.8), 11.5 (7.4 to 15.6), 3.9 (2.6 to 5.1), and 11.2 (9.2 to 13.2) for paddy fields, upland fields, forests and urban areas, respectively (n=5103). The TN specific concentration factor was 1.67 (from a 95% lower limit value of 1.34 to a 95% upper limit value of 2.01), 4.08 (3.64 to 4.51), 0.76 (0.67 to 0.90), and 3.57 (3.38 to 3.76) for paddy fields, upland fields, forests and urban areas, respectively (n=3256). The TP specific concentration factor was 0.146 (from a 95% lower limit value of 0.119 to a 95% upper limit value of 0.172), 0.172 (0.138 to 0.206), 0.044 (0.033 to 0.055), and 0.267 (0.253 to 0.282) for paddy fields, upland fields, forests and urban areas, respectively (n=3256). These specific concentration factors had regional tendencies, such as suburban or rural, intensive or extensive agriculture, and so on.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".