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Process-Based Models of Phenology for Plants and Animals

2017· article· en· W2611891293 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

VenueAnnual Review of Ecology Evolution and Systematics · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsPhenologyEcologyKey (lock)Process (computing)EcosystemBiologyComputer science

Abstract

fetched live from OpenAlex

Phenology is a key aspect of plant and animal life strategies that determines the ability to capture seasonally variable resources. It defines the season and duration of growth and reproduction and paces ecological interactions and ecosystem functions. Phenology models have become a key component of models in agronomy, forestry, ecology, and biogeosciences. Plant and animal process-based phenology models have taken different paths that have so far not crossed. Yet, they share many features because plant and animal annual cycles also share many characteristics, from their stepwise progression, including a resting period, to their dependence on similar environmental factors. We review the strengths and shortcomings of these models and the divergences in modeling approaches for plants and animals, which are mostly due to specificities of the questions they tackle. Finally, we discuss the most promising avenues and the challenges phenology modeling needs to address in the upcoming years.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.452

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 head0.045
GPT teacher head0.322
Teacher spread0.277 · 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