Bibliographic record
Abstract
Wild leek (Allium tricoccum) is a spring ephemeral of northeastern North America. In the Canadian province of Quebec, it is listed as threatened due to human harvesting, and in Gatineau Park its presence is used as an indicator of human impact. Wild leek grows in patches on the forest floor, and before the tree canopy develops its green leaves are clearly visible through the bare branches of deciduous forests, allowing it to be observed with optical remote sensing. This study developed and tested a new method for monitoring wild leek across large geographic areas by integrating field observations, UAV video, and satellite imagery. Three-cm resolution orthomosaics were generated for five <0.1 km2 sites from the UAV video using Structure-from-Motion, segmented, and classified into wild leek (WL) or other (OT) surface types using a simple greenness threshold. The resulting maps, validated using the field observations, had a high overall accuracy (F1-scores between 0.64 to 0.94). These maps were then used to calibrate a linear model predicting the per-pixel percentage cover of wild leek (%WL) from NDVI in the satellite imagery. The linear model calibrated for a Sentinel-2 image from 2018, covering all of Gatineau Park (~361 km2), allowed %WL to be predicted with an RMSE of 10.32. A similar model calibrated for a WorldView-2 image from 2018 was noisy (RMSE = 37.64), though much improved by resampling this image to match the spatial resolution of Sentinel-2, due to MAUP scale effect (RMSE = 13.06). Testing the potential for satellite-based monitoring of wild leek, the %WL prediction errors were similar when a new linear model was developed using the Sentinel-2 image from 2017 (RMSE = 12.84) and when the model calibrated with the 2018 Sentinel-2 image was applied to the 2017 satellite data (RMSE = 16.97). The linear models developed for the Sentinel-2 and WorldView-2 images from 2018 were used to map wild leek cover for Gatineau Park. Both images allowed production of similar wild leek maps that, based on field experience and visual inspection of the imagery, provide good descriptions of the actual distribution of wild leek at Gatineau Park.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".