Bibliographic record
Abstract
What it is for? The Population To Grid (GHS-POP2G) is a flexible tool to produce geospatial population grids in GeoTIFF format from vector census data (polygons or points). The tool operationalises the workflow developed for the production of the Global Human Settlement Layer Population Grid layers (GHS-POP). The principal purpose of the tool is the production of the population grid used as input for the Degree of Urbanisation Grid (GHS-DUG) also produced in the GHSL framework. It is part of the GHS Layers which are extensively used in in crisis management, as they are key variable in disaster alert system. GHSL area used also for disaster response as population density, provide insights on the population in need of emergency relief. Increasingly GHS layers are used to projecting future climate change impact by combining population projections with that of hazard impacts. Urban planners use GHS layers to understand the built-environment within a city, and regional planners use GHS layers to assess the impact of the expansion of cities on other land covers. The layers also inform policy makers that assess the degree of urbanization in the respective countries, or urbanization trends across countries or across regions of interest. The spatial arrangement of built up and green areas within a city is used to quantify climate impact, quality of life in cities based on the amount of green area or the access to transport facilities. Related materials This resource is associated with the Earth Observations Toolkit for Sustainable Cities and Human Settlements. If you want to learn more about it, please, check the EO Toolkit portal (https://eotoolkit.unhabitat.org/), a place where you will find use cases, learning material, and many other tools and resources related to the Sustainable Development Goal 11 and New Urban Agenda.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.190 | 0.175 |
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".