MétaCan
Menu
Back to cohort
Record W1485915805 · doi:10.22230/jem.2013v14n3a556

Trends in renewable resource management in BC

2014· article· en· W1485915805 on OpenAlexaff
Don Sidney Eastman, Ralph Archibald, Rick Ellis, Brian Nyberg

Bibliographic record

VenueJournal of Ecosystems and Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStaffingGovernment (linguistics)BusinessWildlifeNatural resource economicsNatural resourceResource (disambiguation)Christian ministryRenewable resourceResource management (computing)Environmental resource managementEnvironmental planningRenewable energyGeographyEcologyEconomicsPolitical scienceManagement

Abstract

fetched live from OpenAlex

We examined trends in legal responsibilities, budgets and staffing, primarily for the BC government’s renewable resource ministries (forests, fish, wildlife, and parks). Legal responsibilities (complexity) of forest management expanded substantially from 1912 to 2011, almost tripling in the last 25 years. Government expenditures on renewable resources increased steadily from 1975 to 1997, but decreased by approximately half since then. However, the budgets for the remaining “non-resource” sectors of government more than doubled since 1997. The number of professional foresters employed in both government and industry has declined in recent years, more so in industry. Although the total number of professional biologists in the province has increased steadily since 1980, the Ministry of Environment has lost nearly 30 percent of its biologists since 2002. These decreases in funding and staffing jeopardize key management functions, and put the province’s renewable natural resources at increasing risk

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Ecosystems and ManagementSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207