Time series modelling spatiotemporal changes in Biogeoclimatic ecosystem classification (BEC) zones between 1997 and 2019 in West-Central British Columbia, Canada
Bibliographic record
Abstract
Understanding the spatial extent and temporal variability of ecosystem processes is essential for contextualizing land use and land cover change due to disturbance. In this study, we apply an advanced time series modelling method to assess and map ecosystem change and characterize ecosystem cover in west-central British Columbia, Canada. We couple Biogeoclimatic Ecosystem Classification (BEC) zone data with metrics derived from Landsat imagery to model how biogeoclimatic ecosystem cover, interpreted as an indicator of shifting vegetation seasonality , varies over a broad spatiotemporal scale. To do so, we apply the Time-Weighted Dynamic Time Warping (TWDTW) time series modelling approach by relating the spectral characteristics of Landsat data and derived indices from 1997 to 2019. Results highlight important transitions between biogeoclimatic ecosystem classes, with a transition of the interior Douglas-fir Dry to the montane-spruce Dry and the Sub-Boreal Pine to the Spruce zone Dry zones in response to large wildfires in 2003 and 2009. The assessment of ecosystem change across broad spatial and temporal scales is important for assessing the cumulative impacts of changes across highly variable landscapes on essential landscape services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".