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Record W7028042982

The Earth is Shrinking: Advancing Deserts and Rising Seas Squeezing Civilization

2006· article· en· W7028042982 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsChinaDesert (philosophy)CivilizationSquare (algebra)PopulationLand areaAridSatelliteChine
DOInot available

Abstract

fetched live from OpenAlex

Our early twenty-first century civilization is being squeezed between advancing deserts and rising seas. Measured by the land area that can support human habitation, the earth is shrinking. Mounting population densities, once generated solely by the addition of over 70 million people per year, are now also fueled by the relentless advance of deserts and the rise in sea level. The newly established trends of expanding deserts and rising seas are both of human origin. The former is primarily the result of overstocking grasslands and overplowing land. Rising seas result from temperature increases set in motion by carbon released from the burning of fossil fuels. The heavy losses of territory to advancing deserts in China and Nigeria, the most populous countries in Asia and Africa respectively, illustrate the trends for scores of other countries. China is not only losing productive land to deserts, but it is doing so at an accelerating rate. From 1950 to 1975 China lost an average of 600 square miles of land (1,560 square kilometers) to desert each year. By 2000, nearly 1,400 square miles were going to desert annually. A U.S. Embassy report entitled "Desert Mergers and Acquisitions" describes satellite images that show two deserts in north-central China expanding and merging to form a single, larger desert overlapping Inner Mongolia and Gansu provinces. To the west in Xinjiang Province, two even larger deserts -- the Taklimakan and Kumtag -- are also heading for a merger. Further east, the Gobi Desert has marched to within 150 miles (241 kilometers) of Beijing, alarming China's leaders. Chinese scientists report that over the last half-century, some 24,000 villages in northern and western China were abandoned or partly depopulated as they were overrun by drifting sand. All the countries in central Asia -- Afghanistan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan -- are losing land to desertification. Kazakhstan, site of the vast Soviet Virgin Lands Project, has abandoned nearly half of its cropland since 1980. In Afghanistan, a country with a Canadian-sized population of 31 million, the Registan Desert is migrating westward, encroaching on agricultural areas. A U.N. Environment Programme (UNEP) team reports that "up to 100 villages have been submerged by windblown dust and sand." In the country's northwest, sand dunes are moving onto agricultural land, their path cleared by the loss of stabilizing vegetation from firewood gathering and overgrazing. The UNEP team observed sand dunes nearly 50 feet (15 meters) high blocking roads, forcing residents to establish new routes. Iran, which has 70 million people and 80 million goats and sheep, the latter the source of wool for its fabled rug-making industry, is also losing its battle with the desert. Mohammad Jarian, who heads Iran's Anti-Desertification Organization, reported in 2002 that sand storms had buried 124 villages in the southeastern province of Sistan-Baluchistan, forcing their abandonment. Drifting sands had covered grazing areas, starving livestock and depriving villagers of their livelihood.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.007
Scholarly communication0.0060.006
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.003
GPT teacher head0.199
Teacher spread0.196 · 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
Published2006
Admission routes1
Has abstractyes

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