MétaCan
Menu
Back to cohort
Record W2104801284 · doi:10.1139/x08-152

Growth and photosynthetic responses of two coniferous species to experimental warming and nitrogen fertilization

2009· article· en· W2104801284 on OpenAlexvenueno aff
Chunzhang Zhao, Qing Liu

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhotosynthesisHuman fertilizationNitrogenBiomass (ecology)BotanyAgronomySeedlingChlorophyllBiologyHorticultureChemistry

Abstract

fetched live from OpenAlex

The paper studied the short-term effects of experimental warming, nitrogen fertilization, and their combination on growth and photosynthetic performances of Picea asperata Mast. and Pinus tabulaeformis Carr. seedlings. These seedlings were subjected to two levels of temperature (ambient temperature and infrared heater warming) and two nitrogen levels (0 and 25 g N·m –2 ·year –1 ) for 6 months. Warming alone and fertilization alone significantly increased biomass accumulation and photosynthetic performances of both two species. The combination of warming and fertilization stimulated greater accumulation of stem, root, and total biomass and further increased chlorophyll content and net photosynthesis rate (A) in Pinus tabulaeformis seedlings. However, for Picea asperata seedlings, the interaction induced less increment in plant height, total biomass, chlorophyll contents, A, and the efficiency of photosystem than nitrogen treatment alone. These results implied that both climate warming and nitrogen addition were favorable for the early growth of both coniferous species; the interaction of these two factors caused adding or multiplying single-factor effects in the case of Pinus tabulaeformis seedlings but dampening them in the case of Picea asperata seedlings, at least in short time.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.615

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)0.0000.000

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 head0.028
GPT teacher head0.285
Teacher spread0.256 · 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

Citations95
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicPlant Water Relations and Carbon DynamicsFrench-language works237,207