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Record W2612794744

The impact of changing precipitation frequency on hydrology, CO2 exchange, and plant productivity in a temperate poor fen

2017· dissertation· en· W2612794744 on OpenAlexaboutno aff
Danielle D. Radu

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTemperate climatePrecipitationEnvironmental scienceProductivityHydrology (agriculture)GeographyMeteorologyGeologyEcologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Climate change projections for many areas around the Earth show a shift in rainfall patterns towards larger, but less frequent events. The implications for peatlands are hypothesized to be drier surface conditions and a net release of CO2 but there is lack of evidence for this claim. The objective for the project was to investigate the effects of varying rainfall frequencies on peatland hydrology, CO2 exchange, and plant productivity among three different plant communities from a poor fen located in Southern Ontario. In both field and laboratory experiments, vegetation plots and mesocosms were subjected to rainfall treatments that varied in frequency, but not total amount. This thesis shows that decreased precipitation frequency may result in drier near-surface conditions in peatlands, leading to reduced CO2 uptake/ enhanced CO2 release and a plant community shift towards ericaceous shrub dominance.

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 score0.394
Threshold uncertainty score0.784

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 studies0.0010.000
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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

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