Unintended Consequences of Urbanization for Aquatic Ecosystems: A Case Study from the Arizona Desert
Bibliographic record
Abstract
Many changes wrought during the construction of “designer ecosystems” are intended to ensure—and often succeed in ensuring—that a city can provide ecosystem goods and services; but other changes have unintended impacts on the ecology of the city, impairing its ability to provide these critical functions. Indian Bend Wash, an urbanizing watershed in the Central Arizona–Phoenix (CAP) ecosystem, provides an excellent case study of how human alteration of land cover, stream channel structure, and hydrology affect ecosystem processes, both intentionally and unintentionally. The construction of canals created new flowpaths that cut across historic stream channels, and the creation of artificial lakes produced sinks for fine sediments and hotspots for nitrogen processing. Further hydrologic manipulations, such as groundwater pumping, linked surface flows to the aquifer and replaced ephemeral washes with perennial waters. These alterations of hydrologic structure are typical by-products of urban growth in arid and semiarid regions and create distinct spatial and temporal patterns of nitrogen availability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".