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Photosynthesis - Solar Induced Fluorescence relationships in polar ecosystems

2020· article· en· W3088564839 on OpenAlexaff
Kadmiel Maseyk, Holly Croft, Cheryl Rogers, Terenzio Zenone, Walter C. Oechel, Donnatella Zona

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTundraEcosystemPhotosynthesisEnvironmental scienceMossAtmospheric sciencesBogPolarCarbon cycleShrubEcologyBotanyPhysicsBiologyPeat

Abstract

fetched live from OpenAlex

The rapid warming of polar regions is having a demonstrable impact on ecosystem composition and there is a pressing need to understand the carbon cycle implications of these changes. A promising approach for investigating photosynthesis at ecosystem and regional scales involves the remote sensing of Solar Induced Fluorescence (SIF). However, ground-validation of SIF and its association with carbon assimilation and other ecophysiological parameters is largely missing from the polar regions. We will present results of measurements of ground-level SIF and hyperspectral reflectance that were coupled with CO2 exchange measurements in three contrasting polar regions: shrub and bog ecosystems in northern Sweden, wet coastal tundra in Alaska and moss turf in Antarctica. We show good agreement between SIF and photosynthesis across scales, from leaf-level to surface fluxes, but with variable relationships between ecosystem types. Our results show strong potential for using SIF to help understand the impact of change in these regions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.195
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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