Photographs of 60 natural vegetation assemblages in the forest-tundra ecotone near Umiujaq, northern Quebec, Canada
Bibliographic record
Abstract
Photographs of various vegetation types in the forest tundra ecotone near Umiujaq (56.55◦N. 76.55◦W) were taken in September 2015. These photographs correspond to 60 of the 62 vegetation assemblages whose spectral albedo were measured using an SVC HR-1024 radiospectrometer. The spectral range is 346.5 to 2400 nm. Natural vegetation types pictured here are Lichen, Spruce, Dwarf birch with lichen understory (Birch Lichen), dwarf birch with moss understory (Birch Moss) Willows, and Varied low vegetation, classified according to the dominant species at each spot. Data for the 62 spectra are available at DOI 10.5281/zenodo.17416836. Each of the 62 spectra are plotted in the supplementary material, Figures S4 to S9, of the publication: Domine, Bayle, Belke-Brea, Lévesque, Picard (2025) Quantified positive radiative forcing at a greening Canadian Boreal-Arctic transition over the last four decades, Remote Sensing of Environment, 322, 114715, https://doi.org/10.1016/j.rse.2025.114715 The name of each photograph corresponds to the name of the spectrum in DOI 10.5281/zenodo.17416836 and in Figures S4 to S9 of the above publication. Pictures are missing for two spectra: 15sep#6 (willows) and 17sep#29 (spruce). The spot 15sep#6 is similar to spots 15sep#5 and 15sep#7. The spot 17sep#29 is similar to spots 17sep#31 and 17sep#32.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".