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Record W2766003243 · doi:10.1139/cjfr-2017-0258

Reference charts for young stands — a quantitative methodology for assessing tree performance

2017· article· en· W2766003243 on OpenAlexvenueno aff
Lance A. Vickers, David R. Larsen, Benjamin O. Knapp, John M. Kabrick, Daniel C. Dey

Bibliographic record

VenueCanadian Journal of Forest Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersNorthern Research StationU.S. Forest ServiceU.S. Department of Agriculture
KeywordsTree (set theory)QuantileContext (archaeology)SuiteComputer scienceStatisticsEcologyGeographyMathematicsBiology

Abstract

fetched live from OpenAlex

Reference charts have long been used in the medical field for quantitative clinical assessment of juvenile development by plotting distribution quantiles for a selected attribute (e.g., height) against age for specified peer populations. We propose that early stand dynamics is an area of study that could benefit from the descriptions and analyses offered by similar references for various tree measures. Reference charts provide a flexible quantitative framework that would complement traditional methods for assessing tree development. In young, mixed stands, competitive dynamics are, in part, a function of intraspecific, interspecific, and temporal variation in height development. A suite of reference charts can explicitly describe each of these, offering additional context and potentially greater insight into the complex development patterns of young trees. We illustrate this possibility and potential applications by constructing height–age reference charts for several tree species in young, mixed stands within the Missouri Ozarks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.308
GPT teacher head0.445
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations4
Published2017
Admission routes1
Has abstractyes

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