Distribution models of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)
Bibliographic record
Abstract
Long-term ecological field surveys from the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER) have documented the abundance and diversity of microbial mat types across ephemeral glacial meltwater streams in the McMurdo Dry Valleys region of Antarctica. However, field surveys are limited and are incapable of being performed across the entirety of streams within a field season. Therefore, we used remote sensing to examine the distribution of these diverse communities across streams in order to determine whether large scale distribution patterns are similar to those the MCM LTER has already thoroughly studied in situ. As part of our 2018-2019 field campaign, we established two to three 20 x 20 m plots within five different streams (Bowles Creek, McKnight Creek, a relict channel, Canada Stream, and Crescent Stream) in the Fryxell Basin of Taylor Valley. We performed point transect and quadrat field surveys of microbial mat and moss cover within each 20 x 20 m plot. We then used hyperspectral measurements of mat and moss collected in the field, previously archived in “Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019),” in linear spectral mixing models to determine mat and moss coverage in the same 20 x 20 m plots within an atmospherically corrected WorldView-2 satellite image from Dec. 12, 2018. We ground truthed our modeled mat and moss abundances with our field survey coverages and determined the limitations of our methods. We then modeled mat and moss coverage across Huey Creek and Von Guerard Stream to apply our methods to streams without ground truthing measurements. Our results demonstrate the spatial distribution of moss and black, orange, red, and green microbial mat across Fryxell Basin streams. Observations of mat and moss coverage at the basin-wide scale are similar to those seen in localized stream areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".