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Record W1959859660 · doi:10.1002/env.2351

Estimating the transition of individuals between life stages

2015· article· en· W1959859660 on OpenAlexaff
Taly Dawn Drezner, Zvi Drezner, N. Balakrishnan

Bibliographic record

VenueEnvironmetrics · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsMcMaster UniversityYork University
Fundersnot available
KeywordsMetric (unit)Transition (genetics)Standard deviationStatisticsComputer scienceEconometricsVariety (cybernetics)Range (aeronautics)EcologyMathematicsBiologyOperations managementEconomics

Abstract

fetched live from OpenAlex

Over their lifetimes, individuals of a species transition from one stage of their life cycle to the next (for example, a nonreproductive juvenile will mature to a reproductive adult). Estimating the age at which these transitions occur can be complex for a variety of reasons. A fundamental, generalizable way to assess the mean age of transition along with a standard deviation of the distribution of the transition is developed. We propose and statistically develop a method that is easy to use, requires only one data collection period, and provides reliable estimates for the mean and the standard deviation of the transition point. Our results for the case studies for one species, Carnegiea gigantea , using eight independent real‐world datasets, are robust, confirming the validity and usefulness of the proposed technique. We develop an important ecological metric that quantifies the age at which a species transitions from one stage to another in its life cycle. This is a basic ecological metric that is often assumed, but that until now has been difficult to quantify practically for many species. This metric is easy to use (we provide a spreadsheet that does all the calculations) and essential for all life scientists, regardless of species, life form, or ecosystem of study. Copyright © 2015 John Wiley & Sons, Ltd.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.696

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)0.0000.001

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 head0.174
GPT teacher head0.234
Teacher spread0.060 · 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
Published2015
Admission routes1
Has abstractyes

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