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Record W2298836494 · doi:10.14288/1.0097403

First year performance and root egress of white spruce (Picea glauca (Moench) Voss) and lodgepole pine (Pinus contorta Dougl.) seedlings in mechanically prepared and untreated planting sport in North Central British Columbia

2010· article· en· W2298836494 on OpenAlexaboutno aff
Von der Gonna

Bibliographic record

VenueOpen Collections · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsPinus contortaSoftwoodHorticultureSowingBotanyForestryBiologyGeography

Abstract

fetched live from OpenAlex

Root zone temperature and root egress were studied during the first growing season on white spruce and lodgepole pine seedlings planted in various forms of mechanically prepared microsites. Mounded microsites had higher summer soil temperatures and greater diurnal ranges, at a depth of 10 cm, than the patch and control treatments. Mounded microsites, however, showed the greatest response to changes in weather and decreasing solar radiation inputs in the fall, being the first to record soil temperatures below freezing. Seedlings planted in the deep mineral soil over inverted humus mounds created by the Ministry Mounder had significantly greater numbers of new roots greater than 1 cm long than did seedlings planted in patch and control treatments at 45 and 70 days after planting. Seedlings planted in other mound and plowing treatments had high to intermediate numbers of new roots. At 95 days after planting, seedlings planted on all mounded treatments generally had higher root area indices, root dry weights and total dry weights than did seedlings on other treatments. Variation in treatment results over the three spruce sites studied reflect differences in site conditions, primarily soil moisture regimes. High and fluctuating water tables negatively affected seedlings planted in patch and control treatments.

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 score0.639
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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
Published2010
Admission routes1
Has abstractyes

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