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Record W2488178056 · doi:10.5539/jsd.v9n4p151

A Dynamic Model to Assess Carrying Capacity of a Defined System

2016· article· en· W2488178056 on OpenAlexvenueno aff
Yongliang Jin, Richard P. Donovan, William S. Breffle

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaCarrying capacityEnvironmental scienceSystem dynamicsEstuaryResource (disambiguation)PopulationNatural resourceConsumption (sociology)Environmental resource managementResource consumptionNatural resource economicsPopulation growthEnvironmental economicsEcologyComputer scienceEconomicsDemographyBiology

Abstract

fetched live from OpenAlex

<p>A system dynamics method to assess carrying capacity of a defined natural environment is presented. The proposed method seeks to relate per capita resource usage to ranges of population and per capita consumption beyond which the system is not viable relative to population dependent resource constraints. It provides a platform to investigate system behavior through system dynamics simulations where populations change, natural resources decay due to stressor impacts, and feedback occurs via implementation of policy. Application of the model to a case study of Total Maximum Daily Load (TMDL) of phosphorous in Bear Lake, a Lake Michigan estuary (USA), shows the major total phosphorous (P) loading contribution is anthropogenic land use development. Three scenarios are quantitatively explored by assuming changes in land use and/or loading rates. Simulation results show tradeoffs between reduction of total P and land use; economic development can be flexibly evaluated against targets of loading reduction trajectories.</p>

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.314

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 head0.018
GPT teacher head0.200
Teacher spread0.183 · 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 designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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