Repeat of Great Bengal Famine Unlikely Thanks to Fungicides
Bibliographic record
Abstract
Bengal, which prior to the partition of India covered the state of West Bengal in India and Bangladesh, suffered from a calamitous famine in 1943, when two million people died of starvation [1]. Altogether about 3 million people may have died as a result of the famine as disease killed those weakened by starvation [2]. The deaths occurred among the rural population who could not afford to buy rice, which had increased significantly in price due to short supply. World War II had cut off imports of rice from Burma. Shipments of food from Britain, Canada, and the USA were limited due to wartime priorities elsewhere. Food administration in India was the responsibility of provincial governments. Provinces, like Punjab, where food was not in short supply, prohibited rice exports to other regions. The Bengal government made the feeding of the urban Calcutta population a priority and requisitioned rice supplies from rural areas [2]. Many people migrated to the cities in the hope of finding employment and rice. Finding neither, they slowly died of starvation [1].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".