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Record W36191184 · doi:10.1073/pnas.2208360119

Prevalensi TB Laten pada Petugas Kesehatan di RSUP H. Adam Malik

2008· article· en· W36191184 on OpenAlexfundaboutno aff
Ucok Martin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
FundersGovernment of CanadaTula Foundation
KeywordsMedicine

Abstract

fetched live from OpenAlex

Industrial economic models of natural resource management often incentivize the sequential harvesting of resources based on profitability, disproportionately targeting the higher-value elements of the environment. In fisheries, this issue is framed as a problem of "fishing down the food chain" when these elements represent different trophic levels or sequential depletion more generally. Harvesting that focuses on high grading the most profitable, productive, and accessible components of environmental gradients is also thought to occur in the forestry sector. Such a paradigm is inconsistent with a stewardship ethic, entrenched in the forestry literature, that seeks to maintain or enhance forest condition over time. We ask 1) how these conflicting paradigms have influenced patterns of forest harvesting over time and 2) whether more recent conservation-oriented policies influenced these historical harvesting patterns. We use detailed harvest data over a 47-y period and aggregated time series data that span over a century on the central coast of British Columbia, Canada to assess temporal changes in how logging is distributed among various classes of site productivity and terrain accessibility, corresponding to timber value. Most of this record shows a distinct trend of harvesting shifting over time to less productive stands, with some evidence of harvesting occurring in increasingly less accessible forests. However, stewardship-oriented policy changes enacted in the mid-1990s appear to have strongly affected these trends. This illustrates both a profit-maximizing tendency to log down the value chain when choices are unconstrained and the potential of policy choices to impose a greater stewardship ethic on harvesting behavior.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.229
GPT teacher head0.471
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2008
Admission routes2
Has abstractyes

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