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Record W4404615263 · doi:10.3390/f15122062

Seedling Growth Responses to Nutrient and Water Treatments Among Jack Pine Open-Pollinated Families

2024· article· en· W4404615263 on OpenAlexafffund
Pengxin Lu, Francis C. Yeh

Bibliographic record

VenueForests · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsUniversity of AlbertaOntario Forest Research Institute
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeedlingBiologyOpen pollinationNutrientAgronomyBotanyAgroforestryEcologyPollinationPollen

Abstract

fetched live from OpenAlex

Our study, conducted in a controlled greenhouse environment over a single growing season, evaluated the growth of seedlings from 25 open-pollinated families of jack pine (Pinus banksiana Lamb.) under two nutrient levels (20 ppm and 200 ppm) and three water regimes (twice a week, once a week, and once every two weeks). We assessed the effects of seed weight, family, nutrient availability, and water treatments on several growth parameters, including height, root collar diameter, shoot dry biomass, root dry biomass, total dry biomass, growing period length, and shoot-to-root ratio at harvest. We found that seed weight significantly influenced all growth traits, maintaining its effect throughout the growth season, although its impact diminished over time. Jack pine families were more responsive to nutrient treatments than to variation in water availability. Genetic variation was significant for all traits except the shoot-to-root ratio, highlighting the intricate role of genetic makeup in shaping growth responses. The substantial impact of nutrient and water treatments and relatively low heritability estimates suggest that pre-conditioning seedlings through nursery management can optimize shoot-to-root ratios. The minimal family-by-treatment interaction and the consistent performance of families across treatments suggest the potential for selecting high-efficiency genotypes with enhanced nutrient use efficiency and drought tolerance.

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 score0.008
Threshold uncertainty score0.640

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 science0.0000.001
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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