Using variations in High Arctic vegetation spectral properties to predict various types of plant, soil, and environmental variables.
Bibliographic record
Abstract
In comparison to other regions, the High Arctic is experiencing accelerated rates of warming (Meredith et al., 2019). Hyperspectral remote sensing may provide a way to monitor changes in productivity without having to make detailed ground-based measurements. During the 2017 field season researchers on Melville Island, Nunavut, collected in-situ hyperspectral data, plant nutrient concentrations, carbon dioxide gas exchange measurements, and various environmental parameters in a wet-sedge tundra environment. These data were processed with the overall objective of determining if spectral information may be used to quantify changes in productivity across the High Arctic. Using a random forest machine learning algorithm, wavelengths from the hyperspectral data were identified for use in nine vegetation indices (VIs) based on relationships to foliar nitrogen concentrations. Using linear regressions, these VIs were compared to the environmental parameters. Although none correlated significantly to foliar nitrogen, three VIs showed p-values < 0.05 (alpha = 0.05) consistently for the following variables: soil nitrate and ammonia concentrations, net ecosystem exchange (NEE), and gross primary productivity (GPP) values. This shows promise for the use of remote sensing techniques to aid in monitoring the High Arctic. Additional research within this field would help pave way towards increased certainty on the kinds of responses that are in store for these landscapes if warming is to continue at an accelerated rate. This may bring increased monitoring frequency and scale of environmental assessment across the High Arctic, granting communities influenced by warming additional tools to aid in safer regional navigation and improved emergency response preparedness. References Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C. M. M. C., Ottersen, G., Pritchard, H., & Schuur, E. A. G. E. A. G. (2019). Polar Regions. In H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (pp. 203–320). https://www.ipcc.ch/srocc/chapter/chapter-3-2/
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".