Scale Implications in Studies of Landcover Change
Bibliographic record
Abstract
Historical land-cover patterns may have a significant impact on ecosystem function today. To make inferences about the impacts of past land-cover, it is necessary to reconstruct historical patterns visually using data sources ranging from archival information to satellite data. We are exploring different approaches to mapping historical land-cover patterns and their impact on thematic and spatial errors. Our research is focused on three areas in the Frontenac Arch Biosphere (FAB), where we can map land-cover patterns from colonial periods to the present using a variety of data sources. Due to the long temporal scale of our work, we are using data sources collected at different spatial and thematic scales. The data sources include: settlement survey data, census, archival, aerial photos, and satellite imagery. To create maps comparing land-use patterns across time, all of these data sources need to be converted to a common scale.Settlement survey data were converted to maps by classifying colonial concession maps into different land-cover types based on surveyor notes. Air photos (1920, 1940, 1960, and 1980) were digitized at a scale of 1:5000 using the same land classes as were used for the colonial period mapping. Satellite data were used to map land-cover in 2008. We are quantifying errors associated with aggregating and disaggregating data in order to create maps at common spatial resolution.Results from our work will provide estimates of land-cover change over a 200 year period and will include estimates or errors associated with changing the spatial scale of various land-cover sources.
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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 | 0.029 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".