The use of cluster analysis in the classification of similarities in variables associated with agricultural greenhouse gases emissions in OECD countries
Bibliographic record
Abstract
The aim of the research was to group members of the Organization for Economic Co-operation and Development (OECD) into homogeneous subsets for similarities of agricultural variables affecting greenhouse gas emissions. Cluster analysis, which is a tool for exploratory data analysis, was used. This method is based on grouping of elements in a relatively homogeneous class. The most popular non-hierarchical clustering method is k-means. The method is based on an initial a priori assumption of input data set to a predetermined number of classes. In order to verify if the number of clusters was assumed properly, results were compared with another method of cluster analysis – a hierarchical method. Ward’s method of classifying on the basis of minimizing the interclass variance was used. Countries qualified for each cluster derived using k-means were identical to those obtained using Ward’s method. Analysis of the results lead to the conclusion that the geographical location of the countries was key to its inclusion in a cluster this was shown clearly in cluster 1 (Finland, Iceland, Norway, Sweden, Canada), cluster 2 (Austria, Czech Republic, Poland, Slovakia, Switzerland) and cluster 4 (Australia, New Zealand). Group 3 is a 15-element set of countries in predominantly highly industrialized regions.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".