Water as Blue Economy for Sustainable Growth in Pakistan
Bibliographic record
Abstract
: Water as Blue economy is a viable and prudent use of oceans and other water resources for the economic development of a country. Pakistan’s blue economic growth is heavily dependent on; aquatic life, agriculture, biotechnology, energy, health and recreational sector. The paper focuses the relationship of blue economy, i.e. water resources of Pakistan with respect to the several sectors and to investigate water as an economic commodity and highlighting the limiting factors which directly or indirectly affecting the blue economic development of the country thus suggesting the possible solution to overcome the barriers. The secondary data from 1992-2015 has been taken for the analysis of generation of blue capital in Pakistan. The contributing factors impeding the blue economy are; over-exploitation of oceanic resources, deterioration of water quality, lack of awareness and research activities for utilization of marine resources efficiently, bungling of water consumption practice in agriculture, dearth of consistent water ruling system, dilapidation of coastal ecosystem due to human activities, absence of infrastructure and technological advancement for energy production from stored or waste water, and lack of asset for the exploration of useful drugs and by-products from water sediment and in offshore energy production sector. The effective management and governance of available resources, especially for; Irrigation practices, political stability, effective policy framework, tangible investments in water-energy and technological sectors, accessibility of blue resources to the poor and under privilege community and efficient presiding system for the diminution of the synchronization gap between all controlling, monitoring and evaluation are required for viable blue economic development in Pakistan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".