Resident-Level Sufficiency of Urban Green Infrastructure Services in Moscow Across District, Quarter, and Grid Representations
Bibliographic record
Abstract
Large metropolitan green spaces can be described using the total share of land cover types or an ecosystem-services index, but these metrics do not indicate whether vegetation producing the desired services overlaps with people affected by sealing, emissions, heating, surface water runoff, and poor local green space. This paper considers Moscow based on population-oriented ordinally sufficient analysis of the largest urbanized territory. The available data consists of 118 administrative districts, residential quarters, regular grid cells, more than 12 million residents, 841 landscaped objects of green infrastructure, 202.1 km2 of landscaped green infrastructure, 86 nature reserves covering 149.8 km2, and six physical services. Calculation is performed with thresholds and score distributions of sanitary-zone vegetation, stormwater regulation, cooling capacity, cultural green access, residential green provision, and roadside green space. Each block is assessed for adequate land share and adequate resident share such that ecological sufficiency and resident coverage will not be confused. It is concluded that there is little correlation between metropolitan green abundance and resident sufficiency. Regulating services appear to be the largest deficit, with adequate land share rising from 12.0% of district area to 19.2% of grid area, whereas adequate resident share goes up merely from 11.3% to 11.7%. Cultural access and residential provision are better accounted for by quarter level with 48.4% of adequate area and 38.3% of adequate population. Roadside green space shows greatest sensitivity to the grid-level analysis with 37.2% of adequate area and 34.2% of adequate population. Moscow therefore has plenty of green space infrastructure but lacks resident sufficiency due to insufficient overlap of ecological services and human exposure to urban environment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".