Classification of Indian Seaports Using Hierarchical Grouping Method
Bibliographic record
Abstract
India is a major maritime nation with a long coastline, spanning about 7516.6 kilometers, constituting 200 ports in east coast and west coast. East coast and the west coast have 54 and 146 seaports, respectively. Indian ports are classified as Major, Intermediate and, Minor ports; this classification has an administrative significance. Nevertheless, the words: major, intermediate, and minor do not have any relation with the cargo volume throughput. This paper suggests a new approach based on temporal cargo variation to classify a port system. The reason to classify port system based on temporal cargo flow is mainly due to its relevance for cargo operation service, making decisions on freight rate, and service quality performance benchmarking. The key issue faced while attempting for evaluating these measures over a large number of ports is the trouble in comparable data collection from all the port locations and defining the criteria for such evaluations, which will be applicable to all ports. Also, individual port evaluation may not be easy while considering a region’s port system with heterogeneous number of ports. However, this problem can be cut down by classifying ports into certain homogeneous groups. The proposed classification scheme is applied to classify Indian port system. Due to unavailability of data, the application of the proposed method is restricted to 12 Indian ports only. Based on the analysis we propose to classify the 12 Indian seaports into four groups. This classification scheme can be applied to any port system elsewhere.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".