MétaCan
Menu
Back to cohort
Record W6947877673 · doi:10.48336/2dyj-9820

Refining terrestrial biosphere feedbacks to climate change through precise characterization of terrestrial vegetation

2023· article· en· W6947877673 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typearticle
Languageen
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBiosphereClimate changePrimary productionVegetation (pathology)Global changeTerrestrial ecosystemEcosystemContext (archaeology)Carbon cycle

Abstract

fetched live from OpenAlex

Climate change is primarily driven by the human activities of fossil fuel combustion and land use change, which together result in the emissions of greenhouse gases such as carbon dioxide (CO₂). The terrestrial biosphere currently absorbs about a third of total anthropogenic CO₂ emissions, mostly through primary production by vegetation. The continued function of vegetation as a CO₂ sink is uncertain, as climate change has the potential to enhance or restrict the carbon uptake capacity of vegetation. Uncertainty in terrestrial vegetation function in the context of climate change, due in part to a lack of precise observations of leaf biochemistry and function with which to develop models, therefore limits the confidence of climate change projections. In its entirety, this thesis examines the potential for more precise observations of leaf function and their integration across a variety of models and observational scales. The first chapter provides an introductory overview of the subsequent four chapters and how each compliments the other. The second chapter demonstrates the role of the terrestrial biosphere in influencing the relationship between temperature change and cumulative CO₂ emissions. The third chapter provides adaptations to current radiative transfer modelling approaches to improve estimations of leaf biochemical constituents. The fourth chapter applies high spatiotemporal resolution observations of leaf phenology, the timing of leaf emergence and senescence, across North America to predict species-specific leaf phenology patterns under various emissions scenarios throughout the 21st century. The fifth chapter provides an approach to detect declines in ecosystem processes such as carbon uptake using observational leaf phenology networks. These chapter results indicate that 1) uncertainty in the land-borne fraction of carbon emissions contributes largely to uncertainty in the relationship between temperature change and emissions, 2) spectral subdomains and prior estimation of leaf structure improves leaf biochemistry estimations, 3) leaf senescence timing may diverge between boreal and temperate species under a high emissions scenario, and 4) declines in vegetational carbon uptake can be accurately detected using quantitative phenocam-based indicators. The fundamental and technical insights provided through this thesis will facilitate more reliable and functionally resolved projections of terrestrial biosphere feedbacks to climate change.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.113
GPT teacher head0.325
Teacher spread0.212 · 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.

Study designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMemorial University Research Repository (Memorial University)Same topicBiological and pharmacological studies of plantsFrench-language works237,207