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Record W2000436568 · doi:10.1139/x99-255

Heterogeneity in ponderosa pine/Douglas-fir forests: age and size structure in unlogged and logged landscapes of central Colorado

2000· article· en· W2000436568 on OpenAlexvenueno aff
Merrill R. Kaufmann, Claudia M. Regan, Peter M. Brown

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyLoggingRiparian zoneDead treeEcologyForestryBiologyHabitat

Abstract

fetched live from OpenAlex

Tree age and size structures were compared within and among topographic categories in portions of a 35-km 2 unlogged landscape and a comparable adjacent logged landscape. Tree density was generally higher in the logged landscape. One fifth of plots in the unlogged landscape had trees older than 400 years, but no trees older than 400 years remained in the logged landscape plots. Ten recruitment pulses were identified for the unlogged study area, accounting for 49% of all trees measured during 26% of the 421-year survival record. Recruitment pulses in the logged area accounted for fewer trees during a larger amount of time. Most recruitment periods in the unlogged landscape coincided with known past major fires. The mixed-severity historical fire regime created openings that persisted for as long as 148 years. The following components exist in the unlogged landscape: (i) forest patches having a distinct age cap reflecting regeneration following an earlier stand-replacing fire, (ii) uneven-aged forest patches having no evidence of an age cap, (ii) openings created by fire, and (iv) riparian areas. Results suggest that the logged landscape is poised to regain an old-growth age distribution, and tree removal in the logged landscape could restore the size distribution found in the unlogged landscape. However, the unlogged landscape has openings not found in the logged landscape that should be considered in restoration efforts at a landscape scale.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.949

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 science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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

Citations131
Published2000
Admission routes1
Has abstractyes

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