Bibliographic record
Abstract
In order to solve the problems of low categorization accuracy and uneven distribution of the traditional forestry information text classification algorithm,a forestry information text classification algorithm based on Gaussian mixture model(GMM) was puts forward. On the basis of Gaussian mixture model(GMM) and the principle of parametric estimation algorithm,the formula of TFIDF was used to compute text eigenvalue,the constructed feature matrix of forestry information text was reduced in the dimension of eigenmatrix. The Kmeans algorithm should be used,then get the parameters of Gaussian mixture model(GMM) through training of forestry information text,lastly a classifier of Gaussian mixture model(GMM) was established to achieve the goal of faster and accurate classification of forestry information text. The experimental results show that the algorithm has higher accuracy and practicality than the algorithm of neural network and Bayesian and decision tree,and the algorithm pioneer new ideas for studying the forestry information text classification algorithm.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".