Neglected Treasures: Linking Historical Cartography with Environmental Changes in Java, Indonesia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".