MétaCan
Menu
Back to cohort
Record W2519457259 · doi:10.1111/nrm.12105

FAUSTMANN'S FORMULAS FOR FORESTS

2016· article· en· W2519457259 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNatural Resource Modeling · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et Culture
KeywordsAsset (computer security)Investment (military)Natural capitalCapital (architecture)Conjunction (astronomy)Natural resource economicsResource (disambiguation)BusinessEconomicsEnvironmental resource managementEcologyEcosystem servicesComputer scienceEcosystemGeography

Abstract

fetched live from OpenAlex

Abstract The canonical, Faustmannian forest is revisited to sharpen understanding of forests as forms of irreversibly invested capital. Investment and two r‐percent rules are discussed and re‐interpreted. A forest's two natural resources, the stand and the land, act in conjunction as a composite, sunk asset. All returns are attributed to the composite. Nonmarketed or intangible capital is also absorbed into the composite. If capital is comprehensively defined, there is no independent role for the concept of an internal rate of return. A forest provides real options in optimal and suboptimal rotation patterns. Old growth has superficial similarities to an exhaustible resource, but the forest still consists of two resources that, in conjunction, behave comparably to a plantation forest. Nonconvexities inherent in the benefits and costs of forest use indicate that implementing a sustainable program may be very difficult.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.110
GPT teacher head0.233
Teacher spread0.124 · 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