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Record W7071393600

Site quality evaluation of jack pine in Northern Ontario using site-index curves

2017· dissertation· en· W7071393600 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsLandformSite indexCovariateChristian ministryJack pineSoil waterLinear regression
DOInot available

Abstract

fetched live from OpenAlex

Jack pine stem analysis data from 383 fully stocked, mature, undisturbed
\nplots were collected from studies located in four Ontario Ministry of Natural
\nResources regions (Northeast, Northern, North Central, Northwestern) and four
\nbroad landforms (lacustrine, glaciofluvial, morainal, shallow depth to bedrock).
\nComparisons of height-growth patterns in the regions and in the landforms were
\nmade using covariate analysis for nonlinear equations.
\nSeveral different height-growth models and site-index prediction models
\nwere fitted to stem analysis data from 323 plots; the remaining 60 plots were
\nused as verification plots. Results show that height growth was best described by
\na model developed by Ek (1971) and later modified by Newnham (1988). The
\n95% prediction interval for differences (observed - predicted) were within +/-
\n1.39 m and -i-/- 1.59 m for the computation and verification data sets
\nrespectively. A linear model developed by Monserud predicted site index
\nbetter than an exponential or difference equation. But site-index predictions
\nmade indirectly from Newnham's height dependent model were as accurate as
\nusing the linear Monserud model. Early growth before 20 years breast-height X
\nage (BHA) was highly variable and resulted in poor prediction of site index at 50
\nyears. Site index prediction intervals for data older than 20 years BHA were
\nwithin +/- 1.69 m.
\nJack pine height-growth patterns were similar among regions, but some
\nsignificant differences were found among landforms. Jack pine growing on
\ngood sites on all landforms had similar height-growth patterns. But significant
\ndifferences in height-growth patterns were found on poor quality sites; the upper
\nasymptote flattens more sharply on shallow soils when compared to
\nglaciofluvial soils. Height-growth patterns were similar to patterns found for other
\nstudies in North Central Region (Lenthall 1986, Carmean and Lenthall 1989,
\nGoelz and Burk 1992). Results show that the anamorphic curves developed by
\nPlonski (1974) slowed more rapidly after index age as site quality decreased.
\nJack pine height-growth patterns in Ontario are similar to published curves from
\nother areas in Canada with the exception of more rapid early growth on poor
\nquality sites and a flatter upper asymptote on good sites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.301
Teacher spread0.249 · 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 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 routes1
Has abstractyes

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