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Record W3208937889 · doi:10.1080/01431161.2021.1981559

Counting Carbon: Quantifying Biomass in the McMurdo Dry Valleys through Orbital & Field Observations

2021· article· en· W3208937889 on OpenAlexaboutno aff
M. R. Salvatore, J. Barrett, Schuyler R. Borges, Sarah N. Power, Lee F. Stanish, Eric R. Sokol, M. N. Gooseff

Bibliographic record

VenueInternational Journal of Remote Sensing · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPhotosynthetically active radiationEnvironmental scienceBiomass (ecology)Remote sensingSatellite imageryBiosphereGeologyOceanographyEcologyPhotosynthesisBiologyBotany

Abstract

fetched live from OpenAlex

We use correlative field studies and high-resolution multispectral remote sensing data from the WorldView-2 instrument to estimate the abundance of photosynthetically active biomass (photoautotrophs consisting primarily of microbial mats and mosses) in Canada Stream in Taylor Valley, McMurdo Dry Valleys (MDV), Antarctica. In situ field investigations were performed to (1) acquire ground validation targets for atmospherically correcting satellite imagery, (2) derive spectra of “pure” geologic and biological endmembers, (3) estimate photoautotroph cover from remote sensing data, and (4) convert these coverage estimates to biomass using data collected in the field. Our results suggest that, on the morning of 12 December 2018, the Canada Stream system contained more than 3,800 kg of photosynthetically active carbon. Extrapolating our unmixing results to the entirety of the Fryxell basin of Taylor Valley, Antarctica, we model the presence of more than 750,000 kg of photosynthetically active carbon across the landscape and carbon fixation rates roughly equivalent to five hectares of tropical rainforest. The ability to spatially and temporally quantify the amount of photosynthetically active biomass using remote sensing data in the MDV of Antarctica is a revolutionary development that will help elucidate the ecological drivers and environmental responses in this cold desert landscape.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.337
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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