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Record W2989219195 · doi:10.29150/jhrs.v9.2.p80-87

INTERPOLATION METHODS APPLIED TO THE POPULATION GROWTH ESTIMATE

2019· article· en· W2989219195 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.

Bibliographic record

VenueJournal of Hyperspectral Remote Sensing · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsImpact
Fundersnot available
KeywordsPopulationField (mathematics)Interpolation (computer graphics)EstimationPopulation growthComputer scienceContinuationEconometricsData scienceStatisticsGeographyDemographyMathematicsSociologyArtificial intelligenceEconomics

Abstract

fetched live from OpenAlex

Understanding the processes of population change has become important and impregnable for the study in several areas of science, especially the environmental one. In this way, this article intends to use two methods of population estimation with the objective of interpreting and representing the changes in the population dynamics of a municipality, analyzing its current and future conjuncture. Based on a database based on the demographic censuses already carried out, the study identified that between the applied method, being arithmetic and geometric, the second presented a better response and behavior for the case study analyzed, since it allowed an analysis population. Therefore, there is a need for the continuation of research involving mathematical methods to expand the field of environmental, social and analytical studies.

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)0.001
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 score0.466
Threshold uncertainty score0.400

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 studies0.0000.000
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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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