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Record W2081753374 · doi:10.1139/x08-075

The value of timber inventory information for management planning

2008· article· en· W2081753374 on OpenAlexvenueno aff
Bruce E. Borders, William M. Harrison, Michael L. Clutter, Barry D. Shiver, Ray A. Souter

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessValue (mathematics)Profitability indexForest inventoryInventory valuationPlan (archaeology)Forest managementAgricultural economicsForestryFinanceEconomicsGeographyComputer science

Abstract

fetched live from OpenAlex

Timber inventory data is the basis for many monetary transactions related to timber and timberland sale and (or) purchase as well as for development of timber management plans. The value of such data is well known and much appreciated for sale and (or) purchase of standing merchantable timber. Unfortunately, the value of timber inventory data for planning purposes is less well understood. We report on the results of a large simulation study that was undertaken to evaluate the utility and value of timber inventory data for timber management plan development for a typical timberland ownership in the southern United States. Our results indicate that timberland managers are likely producing management plans that do not maximize the profitability of their timberland holdings. Specifically, our results indicate it is likely that timber management organizations that develop timber management plans with stand level data that has a sampling error of 25% are experiencing expected losses in net present value in excess of 170 US$·ha –1 on a large proportion of the acreage found on typical timberland parcels in the southern United States.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.453

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.306
Teacher spread0.260 · 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 designNot applicable
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

Citations42
Published2008
Admission routes1
Has abstractyes

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