Efficacy of Flood relief measures - 2010: A case study of district Layyah, Punjab-Pakistan
Bibliographic record
Abstract
In 2010, Pakistan experienced a massive flood that took the lives of 1985 individuals, in addition to causing huge damage to livestock, shelters, and domestic goods. Multiple local and international organizations extended support to the victims of the 2010 Pakistan flood. Beside relief support, media highly criticized their relief activities. The study was conducted in the district of Layyah, in the Punjab province of Pakistan. The study primarily aims at determining aspects of the flood relating to: ground situation and extent of damages, quality of services provided by the government and non-government organizations (NGOs). The study gathers data and analysis of data was carried out with simple statistical techniques. Ground situation in the country appeared alarming: flood affected 160,000 square kilometer of land, damaged to crop approached US$ 0ne billion, and affected around 20 million people. In the study area 40 % of livestock could not survive, 94.5 % houses were completed abolished and 38.7 % of domestic goods were heavily damaged. District government role was appreciated by 66.4 % of the respondents. Around 50 % of the respondents reported against the performance of the Provincial Disaster Management Authority and National Disaster Management Authority. 96.2 % of the respondents recognized the role of NGOs while respondents suggested working of NGOs through district governments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".