Bibliographic record
Abstract
During urbanization the natural landscape is altered by removal of indigenous vegetation, stripping of topsoil, modification of the soil profile, importation of fill material, compaction of soil layers, and the introduction of impervious surfaces.These actions alter the hydrologic response at the regional and catchment scales, decreasing travel time of overland flow, infiltration, soil water content, and groundwater recharge, and increasing surface runoff volumes, discharge rates, and pollutant loadings.Thesearedirectandquantifiable impacts to the hydrologic cycle manifested at and below the land surface, but there are other impacts not as well understood or quantifiable that occur through the land surface-atmosphere interface.One such impact is the influence of urban development on mesoscale circulations and resulting convection.Hypothesized mechanisms for urban enhancement of convective rainfall include enhanced convergence caused by the urban heat island, drag effects of the built-up surface, and modified microphysical and dynamical processes caused by the introduction of water and cloud condensation nuclei from automobiles and industry.Decades of accumulated observational and modeling evidence has shown that major cities may indeed be influencing convective activity causing modified precipitation patterns.This chapter seeks to corroborate these findings by summarizing evidence that the urbanization of a major coastal city in the United States has resulted in modified rainstorm characteristics within the urbanized area and in the seasonally variant downwind urban-affected
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".