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Record W2963031549 · doi:10.5539/jas.v11n13p20

Hydraulic Traits Performances of Three Pine Species in Tunisia

2019· article· en· W2963031549 on OpenAlexvenueno aff
Sameh Cherif, Olfa Ezzine, Mohamed Larbi Khouja, Zouhaier Nasr

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersEuropean Commission
KeywordsPinus brutiaEnvironmental scienceContext (archaeology)ReforestationAridHydraulic conductivityMediterranean climateBiologyAgronomyEcologyBotanyAgroforestrySoil waterPinus <genus>Soil science

Abstract

fetched live from OpenAlex

Mediterranean forests including Tunisian pine species are threatened by the rising of temperature and decreasing of precipitation. The impact of the increase of aridity differs across species depending on their stomatal and hydraulic responses. In this paper, three pine species: P. halepensis, P. brutia and P. canariensis growing in three different climatic zones: humid, sub-humid and semi-arid, were studied to detect their different responses to drought and guide their selection for reforestation program. Measurements carried out are hydraulic conductivity at point P50, specific conductivity (Ks), midday stem water potential and hydraulic safety margins. Results showed that during summer, vulnerability to embolism, estimated by water potential inducing 50% loss of xylem hydraulic conductivity (P50), is strongly associated with the capacity for drought resistance. Pinus halepensis (P50 = -4.19 MPa) was found to be more resistant to drought than P. brutia and P. canariensis in the semi-arid climate, whereas P. brutia tolerated the humid climate (P50 = -3.7 MPa) and P. canariensis seems more adapted to the sub-humid climate (P50 = -4.08 MPa). Hydraulic safety margins confirmed the conservative behavior of pine species to avoid drought and for maintaining relatively high water potential in dry conditions. These findings help to assess the impact of mid-summer water deficit on pine species in the context of climate change and to select among these species the most resistant for future reforestation programs.

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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.182
Teacher spread0.176 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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