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Record W2956437726 · doi:10.1139/cjfr-2018-0400

Effects of fire rotation interval and overstory type on ambient soil temperatures in ponderosa pine forests in Arizona

2019· article· en· W2956437726 on OpenAlexvenueno aff
David R. Weise, Stephen S. Sackett, Sally M. Haase, Nels G. Johnson

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental sciencePinus <genus>Forest floorPrescribed burnBulk densityClearcuttingForestrySoil waterAtmospheric sciencesSoil scienceBotanyBiologyGeologyGeography

Abstract

fetched live from OpenAlex

Ambient soil temperatures were measured every four weeks from May 1986 to November 1986 at three depths under the organic forest floor in ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in three stand types subjected to periodic prescribed burning. Temperatures at the organic layer – soil surface interface in sawtimber stands were higher and more variable than in pole and saplings stands. Temperature variability reached a maximum in the summer and decreased into the fall and early winter. Soil temperature variability decreased as depth below the surface increased. Three years after the low-intensity prescribed burns, soil temperatures in the burned stands were not significantly different from the those in the unburned controls, suggesting that any effect, though none was detected, of the low-intensity prescribed burns on soil temperature and belowground processes affected by temperature is short-lived in these stands.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.010
GPT teacher head0.255
Teacher spread0.244 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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