Bibliographic record
Abstract
The geographic differences in the transport permeability of the borders of 137 countries and 84 administrative-territorial units of the 1st level of the hierarchy of 6 countries are analyzed. It is measured by the indicator K by dividing the border perimeter (km) by the number of transport crossings (entrances) of the border. There are 10 groups of countries of the world according to the size of their territory. According to the peculiarities of the transport and geographical position in relation to the seas and land, all countries are divided into 13 types. The values of the total transport permeability index vary from 6.4 km (Switzerland) to 3391.3 km (Greenland). Its geographic differences in parts of the world are revealed: the countries of Europe on average have K = 52.6 km, the countries of Asia – 145.2 km, Africa – 177.8 km, America – 206.5 km, Australia and Oceania – 271.0 km. 6 subgroups of countries have been identified according to variations in K values: 1) fully open (the most permeable); 2) relatively open (permeable); 3) semi-open (relatively permeable); 4) semi-closed (medium permeable); 5) relatively closed (little permeable); 6) the most closed (almost impermeable). The values of transport permeability of the borders of 18 provinces of China, 20 states of the USA, 36 regions of Russia, 10 lands of Germany, 2 regions of Canada were calculated. The types of correlation between land and sea transport permeability of the borders are revealed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".