“Financial appraisal of cement industry in India-A comparative study of ultra tech cement & J.K. cement”
Bibliographic record
Abstract
Being the 3rd largest economy in the world, Indian economy, is going to touch new heights in the coming years. Indian Cement Industry will play a significant role in the economic development of the country. The cement industry in India is one of the oldest sectors in India. The industry is driven by the immense growth in the housing sector, the infrastructure development, and construction of transportation systems. Average cement prices are expected to rise by 6% year-on-year (YoY) and 7% on quarter-on-quarter (QoQ) basis across the country despite volume decline in the southern and central regions, a report said. The present research has been aimed at appraising the financial health of cement Industry in general and of the sampled units in particular. The units selected for the present study are Pioneer in their field. Financial management and Accounting techniques have been used. Data is collected from the published financial reports of the company. The major conclusion drawn is that both the companies are using earnings to maximize shareholder's wealth as the market price of both the units under study has increased to a great extent. The inferences drawn from the report could be of immense help to investors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".