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Record W1973578105 · doi:10.5558/tfc82084-1

Performance of planted Engelmann spruce and subalpine fir seedlings in British Columbia's southern mountains

2006· article· en· W1973578105 on OpenAlexaffvenueabout
Cleo C Lajzerowicz, Alan Vyse, Michael J. Jull, Teresa A. Newsome

Bibliographic record

VenueThe Forestry Chronicle · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsBC Research (Canada)
FundersU.S. Forest Service
KeywordsPicea engelmanniiAbies lasiocarpaSeedlingMontane ecologySowingGrowing seasonSubalpine forestForestryBiologyElevation (ballistics)GeographyAgronomyEnvironmental scienceEcologyMathematics

Abstract

fetched live from OpenAlex

We compared survival and growth of planted seedlings of Engelmann spruce and subalpine fir across a range of harvest opening sizes (> 10 ha, 1 ha, 0.2 ha, 0.1 ha, 0.03 ha and individual tree selection) from three silvicultural systems trials in high-elevation spruce – subalpine fir forests in south-central British Columbia. Climatic patterns and growing season air and soil temperatures were similar across sites. Seedling survival decreased with opening size. Local site climates, influenced by aspect and moisture and air drainage, were more influential than elevation. Seedling growth was best in large openings and similar in opening sizes from 1 ha to 0.1 ha. Smaller openings created by group selection and individual tree selection methods were not favourable for successful planting at elevations close to timberline. The two species had similar absolute and relative growth rates but spruce responded more strongly to better growing environments. Key words: planted seedlings, Engelmann spruce, Picea engelmannii Parry ex Engelm., subalpine fir, Abies lasiocarpa (Hook.) Nutt., opening size, elevation effects, silvicultural systems, British Columbia, mountain forests

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 score0.359
Threshold uncertainty score0.820

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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

Citations5
Published2006
Admission routes3
Has abstractyes

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