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Record W1735660206 · doi:10.3138/cart.50.3.2891

Neglected Treasures: Linking Historical Cartography with Environmental Changes in Java, Indonesia

2015· article· en· W1735660206 on OpenAlexvenueno aff
Martin C. Lukas

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyCartographyContext (archaeology)Scale (ratio)JavaEnvironmental changeComparative historical researchEnvironmental historyData scienceArchaeologyComputer scienceHistoryClimate changeOceanographyGeologySocial scienceSociology

Abstract

fetched live from OpenAlex

The historical cartographic material of insular Southeast Asia provides significant potential for research into historical environmental changes. Yet most of this potential has not been realized. This article explores the historical cartography of the Segara Anakan lagoon region on Java's south coast in the context of a shoreline change analysis. It shows how historical cartography supports the analysis of environmental changes and vice versa. Thoroughly exploring the cartographic history of the target region helps to maximize the temporal scale and resolution of analysis; provides insight into the gradual development and replication, but also the ignorance, of cartographic knowledge; and supports an appraisal of the maps' reliability, all of which help to avoid analytical pitfalls. Complementing a quantitative analysis of more recent, relatively accurate cartographic material with a qualitative analysis of very early, less accurate maps and map-makers' records allows the temporal scale of historical environmental research to be extended further into the past. At the same time, it exposes the limits and pitfalls of historical cartographic analyses of environmental change. The author's analysis of the early maps of the Segara Anakan lagoon shows how information from complete but relatively inaccurate maps was repeatedly reproduced by map makers for about a century, while information from more accurate but incomplete maps was consistently ignored.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.268
Teacher spread0.250 · 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 teacher head, not a consensus.

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

Citations9
Published2015
Admission routes1
Has abstractyes

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