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Record W2623519274 · doi:10.1080/02827581.2017.1337920

White spruce recruitment distances relative to lodgepole pine in southern Yukon, Canada

2017· article· en· W2623519274 on OpenAlexaffabout
Wayne L Strong

Bibliographic record

VenueScandinavian Journal of Forest Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsYukon University
Fundersnot available
KeywordsPinus contortaSeral communitySeedlingUnderstoryForestryVegetation (pathology)BiologyEnvironmental scienceBorealBotanyEcologyGeographyEcological successionCanopy

Abstract

fetched live from OpenAlex

Eleven seral, postfire forest stands in southern Yukon (Canada) were sampled to determine where western white spruce (Picea albertiana ssp. albertiana) seedlings occurred with respect to distance to the nearest lodgepole pine (Pinus contorta var. latifolia) tree. Seedling-to-nearest tree distances were assessed at 10-cm increments up to 220 cm. On average, seedlings occurred 54 cm from the nearest pine (n = 490), but peak frequencies were 20–50 cm away, compared to a potential separation distance of 103 cm. Greatest average seedling density occurred 10–20 cm from pine (0.81 m−2), with values between 10 and 120 cm decreasing logarithmatically with increasing distance from pine (r = 0.994, p < .001, n = 11). Spruce seedling densities were <0.02 m−2 beyond 120 cm. The differences in frequency, which represented moderately strong aggregation (Clark-Evans Index 0.34–0.52), and density suggest greater spruce recruitment near lodgepole pine was facilitated by more favorable ecological conditions than further away, for example, greater nutrient availability. The bias in seedling-to-nearest tree distances occurred regardless of stand age (57–165 years), pine density (1599–5935 stems ha−1), or understory vegetation type, although the bias may be weakened by the abundant presence of feathermosses (Hylocomium splendens) on the forest floor.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.528
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.336
Teacher spread0.285 · 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
Published2017
Admission routes2
Has abstractyes

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