Historical vegetation reconstruction of a degraded sub-arctic coastal marsh using Landsat imagery and ancillary data
Bibliographic record
Abstract
In recent decades, increasing numbers of Lesser Snow Geese have severely damaged the sub-arctic coastal marshes on which they feed in summer. Because the geese are an economic, aesthetic and visible species, plans for population control via increased harvest are controversial, and accurate vegetation maps are needed both for management decision-making and for public education. Documentation of the extent, pattern, and timing of habitat damage over large spatial and temporal scales requires reconstruction of the previously-existing vegetation. Satellite imagery is increasingly used for mapping and monitoring vegetation change over large areas, and archives of Landsat imagery provide consistent, spatially-extensive historical data. Previously-existing vegetation at La Pérouse Bay, Manitoba, was mapped using unsupervised classification of a 1984 Landsat TM image and ancillary information. Using a commercial clustering algorithm, sixty-three spectral clusters were produced and subsequently identified using a combination of historical and current aerial photos, current vegetation data, and knowledge of local vegetation trajectories in time. These initial clusters were then aggregated into 14 classes, resulting in a detailed map of vegetation as it was before degradation by the geese. The map was compared to earlier results documenting change in the study area. Vegetation degradation between 1973 and 1993 occurred earliest and most extensively in the salt marsh and other classes containing salt marsh vegetation. Map accuracy was assessed with current vegetation data collected independently at a number of precisely located points.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".