MétaCan
Menu
Back to cohort
Record W2116405917 · doi:10.1139/x05-143

Forest landowner decisions and the value of information under fire risk

2005· article· en· W2116405917 on OpenAlexvenueno aff
Gregory S. Amacher, Arun S. Malik, Robert G. Haight

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLand tenureValue (mathematics)BusinessPrescribed burnNatural resource economicsEnvironmental scienceGeographyForestryEconomicsStatisticsMathematics

Abstract

fetched live from OpenAlex

We estimate the value of three types of information about fire risk to a nonindustrial forest landowner: the relationship between fire arrival rates and stand age, the magnitude of fire arrival rates, and the efficacy of fuel reduction treatment. Our model incorporates planting density and the level and timing of fuel reduction treatment as landowner decisions. These factors affect, among other things, the loss a landowner incurs should fire arrive before harvesting. The value of information depends on the nature and combination of mistakes a landowner makes, the relationship between fire arrival and stand age, and on whether the landowner undertakes fuel treatment and values nontimber benefits. Information of various types is of most value to a landowner who does not undertake fuel treatment. The value of information about the magnitude of fire risk is also more than twice as high when the landowner underestimates fire risk, rather than overestimating it. For a landowner who undertakes fuel treatment but makes multiple mistakes, the asymmetry between overestimating and underestimating fire risk and efficacy of fuel reduction is even more pronounced.

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)0.001
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 score0.228
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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

Citations25
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Management and PolicyFrench-language works237,207