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Record W2497245627 · doi:10.1017/chol9780521812894.006

Land Use and the Transformation of the Environment

2005· book-chapter· en· W2497245627 on OpenAlexaff
Elinor G. K. Melville

Bibliographic record

VenueCambridge University Press eBooks · 2005
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsYork University
Fundersnot available
KeywordsGeographyPortugueseMediterranean climateAgroforestryBiologyArchaeology

Abstract

fetched live from OpenAlex

The Spaniards and the Portuguese came to the New World with the means to reproduce their societies and their landscapes. On his second voyage to the Caribbean in 1493, for example, Columbus brought 1,500 men in 17 ships to settle in Española. He also brought seeds to grow wheat, as well as vegetables, fruit trees and grape vines, horses, cattle, sheep, goats, and pigs. Over the following decades, as the Spaniards spread across the islands of the Caribbean Sea and into the mainland, they took with them their companion species – their “portmanteau biota,” as Alfred Crosby has called them. Even when their aim was purely military, the conquistadores traveled with at least horses and war dogs; but when they settled they made every effort to grow the plants and raise the animals so necessary for a proper (Mediterranean) diet: wheat for bread; olive trees and grapes for oil and wine; sheep and cattle for meat, milk, and cheese, wool for warm clothing, and leather for saddles and bags; and so forth. And they grew sugar cane, with a view to producing sugar for export to Europe. The Portuguese, intent at first on trade and later settlement, followed closely with sugar and slaves, cattle and horses, and planted their engenhos on the eastern coasts of South America. In the process of developing the specialized ecosystems that maintained these plants and animals, the Spanish and the Portuguese transformed not only the ways in which the land was used and hence the landscapes, but the physical environment itself. The invaders were successful, Crosby suggests, where their portmanteau biota thrived and transformed the indigenous worlds.

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.014
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.015
GPT teacher head0.149
Teacher spread0.133 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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