What Is the Impact of Tectonic Plate Movement on Country Size? A Long-Term Forecast
Bibliographic record
Abstract
The Earth’s surface is under permanent alteration with the area of some nations growing or shrinking due to natural or man-made processes, for example sea level change. Here, based on the NUVEL 1A model, we forecast (in 10, 25, and 50 years) the changes in area for countries that are located on the border of the major tectonic plates. In the analysis we identify countries that are projected to gain or lose land due to the tectonic plate movement only. Over the next 50 years, the global balance of area gains (0.4 km2) and losses (12.7 km2) is negative. Thus, due to the movements of lithospheric plates, the land surface of the Earth will decrease by 12 km2 in 50 years. Overall, the changes are not that spectacular, as in the case of changes in sea/water levels, but in some smaller countries, projected losses exceed a few thousand square metres a year, e.g., in Nepal the losses exceed 10,000 m2 year−1. Methodologically, this paper finds itself between metric analysis and essay, trying to provoke useful academic discussion and incite educators’ interests to illustrate to students the tectonic movement and its force. Limitations of the used model have been discussed in the methodology section.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".