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Record W1561536661

Growth of 10 Tree Species in Relation to Location and Microclimatic Gradients in a Strip Shelterwood

2009· article· en· W1561536661 on OpenAlexaboutno aff
Kazi L. Hossain, Philip G. Comeau

Bibliographic record

VenueDigital Commons - USU (Utah State University) · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceTree (set theory)Atmospheric sciencesForestryGeographyMathematicsGeology
DOInot available

Abstract

fetched live from OpenAlex

Strip shelterwood systems are used in some areas to favour establishment of intolerant and moderately tolerant tree species or to facilitate harvesting. There is substantial variation in microclimate within cleared strips, which can influence survival and growth of regeneration. The growth of the established regeneration depends on the microclimate (light, soil moisture, air and soil temperature) at different locations in gaps. This poster presents results from a study being conducted at Nakusp in Southern BC, Canada. The purpose of this study is to improve our understanding of the microclimatic pattern after gap creation and its influence on the growth of planted seedlings of 10 native tree species. Preliminary results, collected 13 to 14 years after planting, show gradual increase of light and air temperature from the south to the north edge of the gaps. Soil moisture stress also increased from the south to the north edge. Species are showing variable growth response to these gradients. Shade intolerant species performed better at the centre and north edge of the gap, while shade tolerant species have survived and established well under the canopy and near the edges. Among the tree species evaluated, Western hemlock and Engelmann spruce were best suited to the south edge and in the intact forest, while Douglas fir performed best at north edge and inside the opening. Regardless of their shade tolerance classification, all the species grow best near the centre of the opening, where light levels are highest.

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 score0.042
Threshold uncertainty score0.341

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.176
Teacher spread0.168 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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