Prediction method of regional economic development level based on SOM algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a vital but time-consuming task, regional GDP forecasting has high expectations for forecast accuracy in order to provide better guidance and suggestions for the region's economic development in the future.Most traditional GDP pre-methods are linear, but their accuracy has gradually been difficult to meet the demand due to factors such as nonlinearity and GDP uncertainty.This paper proposes a method for forecasting the level of regional economic development based on the SOM algorithm in order to improve forecast reliability and accuracy.These two-dimensional neural networks, with their layers of neurons arranged in a two-dimensional topology, are combined in this paper to create a self-organizing feature map model (SOM).The ANN model converts the SOM's output and outputs the model's final classification result directly.Using this model's algorithm, the model's performance can be greatly improved, while noise samples can be eliminated and the model's accuracy greatly improved.This study predicts future regional GDP using data on prefecture-level city GDP as the regression independent variable from 2001 to 2021.This paper's method successfully predicts the level of economic development in a region, as demonstrated by a large number of experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it