MétaCan
Menu
Back to cohort
Record W2267456098 · doi:10.1139/cjfr-2015-0447

The responses of Scots pine seedlings to waterlogging during the growing season

2016· article· en· W2267456098 on OpenAlexvenueno aff
Tapani Repo, Samuli Launiainen, Tarja Lehto, Sirkka Sutinen, Hanna Ruhanen, Juha Heiskanen, Ari Laurén, Raimo Silvennoinen, Elina Vapaavuori, Leena Finér

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsScots pineTranspirationGrowing seasonPhotosynthesisShootHorticultureEnvironmental scienceAgronomyBotanyBiologyBorealPinus <genus>Ecology

Abstract

fetched live from OpenAlex

In the future management and sustainable use of boreal forests, it is crucial to consider the rate and strength of tree responses to an elevated water table and the concurrent oxygen limitations, especially in peatlands. We examined the response dynamics of 7-year-old Scots pine (Pinus sylvestris L.) seedlings to a 5-week waterlogging (WL) treatment during a growing season in a root lab (dasotron) experiment. WL took place after shoot elongation had ended but while growth of the trunk diameter was still in progress. Trunk sap flow and needle water potential started to decrease immediately after the onset of WL, while the first signs in needle gas exchange — seen as decreases in the potential efficiency of photosystem II, the rates of light-saturated net assimilation and transpiration, and increased needle respiration — were observed after 3 weeks of WL. New needles responded to WL more strongly than the old ones. Drainage with consequent re-oxygenation of the soil caused a further decrease in sap flow. We conclude that through negative feedback on transpiration and net photosynthesis, WL during the growing season is harmful for Scots pine, leading to potential growth losses or even dying of trees within a few weeks of WL.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
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 head0.037
GPT teacher head0.271
Teacher spread0.234 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicPlant responses to water stressFrench-language works237,207