Monitoring land cover change in the Lake Superior basin
Bibliographic record
Abstract
Consistent, repeatable and broadly applicable land use, land cover data is needed across the Lake Superior basin to facilitate ecosystem condition assessment and trend analysis. Such a data set collected regularly through time could inform and focus field monitoring efforts, and help prioritize restoration and mitigation efforts. Unfortunately, few data sets exist that are bi-nationally consistent in time, classification method, or resolution. To this end, we integrated land cover data across both the Canadian (Ontario Provincial Land Cover data) and US portions (National Land Cover Data) of the Lake Superior basin for two time steps (approximately 1992 and 2001) roughly one decade apart. After harmonizing landcover classes across the two datasets we compared the explicit amount and relative amount (total hectares and proportion of each area as percents) for each of the common land cover classes that occurred across the two time steps for the entire Lake Superior basin, for the U.S. portion of the Lake Superior basin only, and for the Canadian portion of the Lake Superior basin only. We also compared land cover change for the entire basin within a 1 km and a 10 km buffer of the Great Lakes shoreline. We then summarized and compared these land cover types for each time period across a common set of watersheds derived from elevation data (Hollenhorst et al., 2007) for the entire Lake Superior basin. This allowed us to identify and quantify the types of change occurring generally across the entire basin, more specifically across both the U.S. and Canadian portions of the basin, and more explicitly for near coastal areas and watersheds across the entire basin. Noteworthy changes were detected across the basin, particularly an increase in mixed forest types and a corresponding decrease in coniferous forest types.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".