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Record W3031674357 · doi:10.1139/cjfr-2019-0437

Interactive effects of water and CO<sub>2</sub> on light response efficiency and gas exchange traits in pine (<i>Pinus</i>) and spruce (<i>Picea</i>) species

2020· article· en· W3031674357 on OpenAlexaffvenue
John E. Major, Ale× Mosseler

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsPicea abiesBotanyBeechPhotosynthesisWater-use efficiencyBiologyEnvironmental scienceEcologyHorticulture

Abstract

fetched live from OpenAlex

Photosynthetic light response curves were parameterized for eight species in two genera, Pinus and Picea, grown in a 2 × 2 factorial of atmospheric CO2 and soil moisture treatments. Four of the pines and three of the spruces are native to eastern North America, and the fourth spruce, Norway spruce (Picea abies (L.) Karst.), is native to Europe. There was a significant genus × CO2 interaction in apparent quantum efficiency (AQE): spruce AQE was greater under ambient CO2 (CO2) than elevated CO2 (eCO2), but pine AQE were equal. Under drought treatment (DR), AQE declined for both genera. Assimilation at light saturation (Alsat) was greater for spruces than pines, and for both genera, Alsat decreased under eCO2 and DR. Water-use efficiency was greater for pines than spruces and greater for pines and unchanged for spruces under DR. Examining AQE and Alsat change (%) from aCO2 to eCO2, there was a significant positive relationship to biomass growth stimulation (%) across species. These relationships support the theory of sink (biomass growth) regulation of assimilation traits and also the importance of needle nitrogen. Our results in response to eCO2 and DR suggest a shift toward increased use of pines in forest management for eastern North America.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

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 studies0.0000.000
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 head0.013
GPT teacher head0.234
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 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest Research→Same topicPlant Water Relations and Carbon Dynamics→French-language works237,207→