Utilizing Transfer Learning with Artificial Intelligence for Scaling-Up Lichen Coverage Maps
Bibliographic record
Abstract
Lichen mapping is essential for sustainable caribou and lichen conservation. Previous studies have used artificial intelligence to create lichen coverage (%) maps using a scaling-up methodology. This paper proposes a transfer learning approach to lichen mapping, where the model weights from a lichen coverage (%) neural network trained on Quebec and Labrador data were used for training a northern Ontario model. The training and evaluation dataset consisted of 22 10-m lichen maps from Geraldton, Martin Falls, and Peawanuck Ontario, aligned to Sentinel-2 imagery. The model with transfer learning outperformed a similar neural network with randomized initial weights and a random forest model, despite predicting lichen in different ground conditions than Quebec and Labrador. A northern Ontario lichen map was created using a Sentinel-2 mosaic, displaying high amounts of lichen surrounding Peawanuck, Ontario. Whenever feasible, transfer learning approaches should be considered when organizing regional vegetation coverage (%) maps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".