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Record W2170418049 · doi:10.1139/x04-084

Comparison of charcoal and tree-ring records of recent fires in the eastern Klamath Mountains, California, USA

2004· article· en· W2170418049 on OpenAlexvenueno aff
Cathy Whitlock, Carl N. Skinner, Patrick J. Bartlein, Thomas A. Minckley, Jerry Mohr

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsCharcoalDendrochronologyFire historyPhysical geographySedimentGeographyEnvironmental scienceWatershedForestryArchaeologyClimate changeHydrology (agriculture)GeologyOceanographyPaleontology

Abstract

fetched live from OpenAlex

Fire-history reconstructions are based on tree-ring records that span the last few centuries and charcoal data from lake-sediment cores that extend back several thousand years. The two approaches have unique strengths and weaknesses in their ability to depict past fire events and fire regimes, and most comparisons of these datasets in western conifer forests have focused on sites characterized by high-severity crown fires. Tree-ring and charcoal data spanning the last 300 years in four watersheds in the montane forests of the Klamath Mountains provided an opportunity to compare the records in a fire regime of frequent low- to moderate-severity surface events. The charcoal data were obtained from small lakes, and tree-ring records were derived from fire-scar chronologies at multiple sites within each watershed. The comparison indicates that the tree-ring records detected individual fires not evident in the lake-sediment profiles, whereas the charcoal data disclosed variations in fuel loading and general levels of burning at broader spatial scales. Regional burning in the late 19th and early 20th centuries was evident in the lake-sediment records, and both datasets registered a decline in fire activity in the late 20th century. Thus, the two types of data provide complementary as well as supplementary information on past fire conditions.

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)0.002
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 score0.637
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.055
GPT teacher head0.329
Teacher spread0.274 · 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

Citations59
Published2004
Admission routes1
Has abstractyes

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