Impact of a Growing Population in Agricultural Resource Management: Exploring the Global Situation with a Micro-level Example
Bibliographic record
Abstract
A decade ago, David Pimentel and his associates (1998) reported to us that at least ten million hectares of arable land were being eroded and also abandoned throughout the world every year and consequently to compensate such a loss, a huge amount of replacement is claimed from forests and other sources for agriculture and human settlement. In the meantime, world population exceeded 6 billion in the year 1999, and the projected data indicate that it is going to be almost 9 billion within the next 40 years. For that reason, the demographers and environmentalists have highlighted that the main challenge for environmental management throughout the world today is to determine our planet’s capacity to sustain such a huge amount of burgeoning human population. The paper thus assesses specifically the impact of growing population on agricultural resources around the world, creating depressing pressure on sustainable environmental management. To exemplify such a trend of agricultural land use, the paper incorporates a detailed example from an ethnographic case study on indigenous land-use practices and the experiences associated with modern cultivation for adapting to adverse situations caused by severe impact of a growing population in the agricultural sector in rural Bangladesh.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".