“Financial appraisal of cement industry in India-A comparative study of ultra tech cement & J.K. cement”
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it