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Record W4389795771 · doi:10.3138/cart-2023-0006

The Image of Precision: The Case of the <i>Longitudinal</i> Amazon

2023· article· en· W4389795771 on OpenAlexvenueno aff
Roberto Chauca

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsnot available
Fundersnot available
KeywordsAmazon rainforestHydrographyCartographyGeographyRepresentation (politics)GeoreferenceMartyrHistoryArchaeologyPhysical geographyPoliticsEcology

Abstract

fetched live from OpenAlex

A set of maps from the middle of the sixteenth century featuring the New World, such as those by Jean Rotz (1542), Giacomo Gastaldi (1546), and Jean Bellère (1554), presented a peculiarity regarding the delineation of the Amazon River. Instead of having a latitudinal direction, as dictated by modern cartographic conventions, the Amazon had a longitudinal orientation. This is often assumed to be an inaccurate cartographic representation of the river. However, these maps were rather precise insofar as, according to contemporaneous accounts of South America provided by scholars such as Peter Martyr, Gonzalo Fernández de Oviedo, and Francisco López de Gómara, the Amazon had a potential longitudinal course. In this sense, the author aims to explain the accuracy of some of the early modern cartographies of Amazonia by placing these maps in dialogue with geographic and hydrographic descriptions of the river that circulated at that time. Consequently, this work re-evaluates the notion of precision when applied to the study of the early European mapping of the Amazon River.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.022
Scholarly communication0.0090.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.334
Teacher spread0.318 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicColonialism, slavery, and tradeFrench-language works237,207