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Record W3012535449 · doi:10.1139/cjfr-2019-0163

Dynamics of fine-root production and mortality of Scots pine in waterlogged peat soil during the growing season

2020· article· en· W3012535449 on OpenAlexvenueno aff
Tapani Repo, Timo Domisch, Jouni Kilpeläinen, Sirpa Piirainen, Raimo Silvennoinen, Tarja Lehto

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsScots pinePeatGrowing seasonBiologyBorealDitchXylemTaigaAgronomyWaterlogging (archaeology)HorticultureBotanyEnvironmental sciencePinus <genus>Ecology

Abstract

fetched live from OpenAlex

Excess water in the rooting zone critically reduces tree growth and may even kill trees; however, the relative importance of damage to roots versus aboveground parts and the time course of damage are not well understood. We studied the dynamics of fine-root growth and mortality of 7-year-old Scots pine (Pinus sylvestris L.) saplings affected by a 5-week period of waterlogging (WL) during the growing season. Two out of six WL-exposed saplings survived the treatment. After 1–2 weeks of WL, the mortality of the first-order short roots (usually mycorrhizas) started to increase and the production of these roots started to decrease. WL decreased the longevity of short and long roots. Total root length (especially of fine roots with a diameter &lt; 0.5 mm), specific fine-root length, total root dry mass (including stump), and reverse-flow root hydraulic conductance were lower in WL saplings than in control saplings at the end of the experiment; however, several root traits were similar in control and surviving WL saplings. Because of the high importance of fine roots for tree growth and carbon sequestration, their responses to elevated water tables should be considered in sustainable use and management of boreal peatland forests, for example, by continuous cover forestry and (or) ditch network maintenance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.982

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 head0.045
GPT teacher head0.265
Teacher spread0.220 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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