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Growth allocation in short and long shoots in <i>Betula glandulosa</i> Michx.

2015· other· en· W6960433086 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2015
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsShootSquare (algebra)LogarithmBlock (permutation group theory)Randomized block design

Abstract

fetched live from OpenAlex

<p>The similar ratio of <i>Betula glandulosa</i> Michx. short to long shoots (±95% CI) in unbrowsed and moderately browsed plots in 2009 and under all browsing pressure in 2010 suggests conversion of short shoots into long shoots. The number of short shoots and long shoots was estimated on marked ramets in August after two years of simulated caribou browsing and fertilisation near Deception Bay (Nunavik, Québec, Canada). The analysis was performed using a linear mixed model with block (n = 5) and all interactions involving block as random factors and year as a repeated measure. Transformations were applied, square root and natural logarithm for long and short shoots, respectively. Means and CI presented are untransformed. Different letters over bars indicate a posteriori least square mean differences in simulated browsing level for each year on transformed data; (α = 0.05). For long shoots, the a posteriori test was performed on transformed data, on a nearly significant interaction, (α = 0.05).</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.313
Threshold uncertainty score0.984

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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