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Record W2503015936 · doi:10.1057/9780230234253_12

Environmental and Resource Implications of Chinese Growth: Current Trends and Future Prospects

2009· book-chapter· en· W2503015936 on OpenAlexaboutno aff
Xizhe Peng, Kexi Pan, Juan Yu

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWater Resources and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resourceArable landPer capitaChinaNatural resource economicsEconomic shortagePopulation growthQuarter (Canadian coin)Consumption (sociology)Resource (disambiguation)Natural (archaeology)PopulationGeographyAgricultural economicsEconomicsBusinessAgricultureEcology

Abstract

fetched live from OpenAlex

China is a country rich in natural resources. However, due to its large population size, the per capita availability of many essential natural resources is much less than the corresponding world average. For instance, China’s per capita availability of water, arable land, and mineral resources is only 28, 32, and 50 percent of the corresponding global levels. As the result of her remarkable economic growth, China’s consumption of natural resources has increased very rapidly over the last few decades. Among 45 major natural resources that are essential for industrial development, one quarter has already been in shortage of supply. The shortage of key natural resources is viewed as one of the critical constraints on China’s future economic growth. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.007
GPT teacher head0.205
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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