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Record W2831908411 · doi:10.1139/cjb-2018-0065

Contrasting root overwintering strategies of perennial wetland monocots

2018· article· en· W2831908411 on OpenAlexaffvenueabout
Tim Nieman, Yvonne Hoogzaad, Susara J.E. Marcotte, Peter Ryser

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsLaurentian University
Fundersnot available
KeywordsOverwinteringBiologyPerennial plantEvergreenTemperate climateBotanyGrowing seasonDeciduousHerbaceous plantAnnual growth cycle of grapevinesEcologyNutrientEcosystemAgronomyShoot

Abstract

fetched live from OpenAlex

Root turnover is an important contributor to ecosystem nutrient and carbon cycling, but seasonal aspects of root mortality are not well known. This study tests the hypothesis that in strongly seasonal climates, such as in Northern Ontario, Canada, perennial wetland monocots fall into two distinct categories with respect to their root overwintering strategy: complete senescence or survival over the winter. Root survival in late winter and early spring was tested for a total of 26 species using vitality staining with tetrazolium chloride. Root survival in spring was either over 85% (18 species) or 0% (8 species). Lateral root survival was marginally lower than that of basal roots. In some species, low nutrient supply slightly increased root winter mortality, but did not change the seasonal pattern. We conclude that in a northern temperate climate, the overwintering strategies of roots of herbaceous monocots are binary: either avoidance or tolerance of the long unfavourable season, similar to deciduous and evergreen leaves among woody plants. Roots do not gradually die during the unfavourable season, but either completely senesce in the autumn or survive the winter. The distinct root overwintering strategies presumably affect the species’ resource economics and ecosystem processes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.225
Teacher spread0.217 · 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 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

Citations16
Published2018
Admission routes3
Has abstractyes

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