Bibliographic record
Abstract
# Worldmap Worldmap is a brane package for creating maps of the world using Geopandas. Each country can be given a value representing a color in the red, yellow and green range. ## Installation If on Linux or MacOS first run: ``` chmod +x run.py ``` Otherwise/then: ```console brane build container.yml brane push worldmap 1.0.0 ``` Or install using the brane import function: ```console brane import lucasdegeus/braneWorldmap --kind ecu ``` ## Usage Input parameters are: * countries: an array of country names * values: an array of values corresponding with the countries * legend_name: name below the color bar * path: path to save file ```brane import worldmap; let countries := ["Canada", "United States of America"]; let values := [100.0, -50]; let legend_name := "Sentiment"; let path := "/data/wordmap.png"; create_map(values, countries, legend_name, path); ``` ## Notes Countries and values are matched using their index. Lowest value in the array is represented using the color red, highest value by green and missing countries by grey. These values can be positive, negative, floats or integers. Countries should be provided with names according to this list: ['Fiji', 'Tanzania', 'W. Sahara', 'Canada', 'United States of America', 'Kazakhstan', 'Uzbekistan', 'Papua New Guinea', 'Indonesia', 'Argentina', 'Chile', 'Dem. Rep. Congo', 'Somalia', 'Kenya', 'Sudan', 'Chad', 'Haiti', 'Dominican Rep.', 'Russia', 'Bahamas', 'Falkland Is.', 'Norway', 'Greenland', 'Fr. S. Antarctic Lands', 'Timor-Leste', 'South Africa', 'Lesotho', 'Mexico', 'Uruguay', 'Brazil', 'Bolivia', 'Peru', 'Colombia', 'Panama', 'Costa Rica', 'Nicaragua', 'Honduras', 'El Salvador', 'Guatemala', 'Belize', 'Venezuela', 'Guyana', 'Suriname', 'France', 'Ecuador', 'Puerto Rico', 'Jamaica', 'Cuba', 'Zimbabwe', 'Botswana', 'Namibia', 'Senegal', 'Mali', 'Mauritania', 'Benin', 'Niger', 'Nigeria', 'Cameroon', 'Togo', 'Ghana', "Côte d'Ivoire", 'Guinea', 'Guinea-Bissau', 'Liberia', 'Sierra Leone', 'Burkina Faso', 'Central African Rep.', 'Congo', 'Gabon', 'Eq. Guinea', 'Zambia', 'Malawi', 'Mozambique', 'eSwatini', 'Angola', 'Burundi', 'Israel', 'Lebanon', 'Madagascar', 'Palestine', 'Gambia', 'Tunisia', 'Algeria', 'Jordan', 'United Arab Emirates', 'Qatar', 'Kuwait', 'Iraq', 'Oman', 'Vanuatu', 'Cambodia', 'Thailand', 'Laos', 'Myanmar', 'Vietnam', 'North Korea', 'South Korea', 'Mongolia', 'India', 'Bangladesh', 'Bhutan', 'Nepal', 'Pakistan', 'Afghanistan', 'Tajikistan', 'Kyrgyzstan', 'Turkmenistan', 'Iran', 'Syria', 'Armenia', 'Sweden', 'Belarus', 'Ukraine', 'Poland', 'Austria', 'Hungary', 'Moldova', 'Romania', 'Lithuania', 'Latvia', 'Estonia', 'Germany', 'Bulgaria', 'Greece', 'Turkey', 'Albania', 'Croatia', 'Switzerland', 'Luxembourg', 'Belgium', 'Netherlands', 'Portugal', 'Spain', 'Ireland', 'New Caledonia', 'Solomon Is.', 'New Zealand', 'Australia', 'Sri Lanka', 'China', 'Taiwan', 'Italy', 'Denmark', 'United Kingdom', 'Iceland', 'Azerbaijan', 'Georgia', 'Philippines', 'Malaysia', 'Brunei', 'Slovenia', 'Finland', 'Slovakia', 'Czechia', 'Eritrea', 'Japan', 'Paraguay', 'Yemen', 'Saudi Arabia', 'Antarctica', 'N. Cyprus', 'Cyprus', 'Morocco', 'Egypt', 'Libya', 'Ethiopia', 'Djibouti', 'Somaliland', 'Uganda', 'Rwanda', 'Bosnia and Herz.', 'Macedonia', 'Serbia', 'Montenegro', 'Kosovo', 'Trinidad and Tobago', 'S. Sudan']
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.386 | 0.392 |
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".