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Applying Movement Ecology to Marine Animals with Complex Life Cycles

2017· review· en· W2714855017 on OpenAlexafffund
Richard M. Allen, Anna Meta×as, Paul V. R. Snelgrove

Bibliographic record

VenueAnnual Review of Marine Science · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsMemorial University of NewfoundlandDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsMovement (music)EcologyBiological dispersalResource (disambiguation)Life historyConstruct (python library)Scale (ratio)InterdependencePopulationDiversity (politics)BiologyGeographySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Marine animals with complex life cycles may move passively or actively for fertilization, dispersal, predator avoidance, resource acquisition, and migration, and over scales from micrometers to thousands of kilometers. This diversity has catalyzed idiosyncratic and unfocused research, creating unsound paradigms regarding the role of movement in ecology and evolution. The emerging movement ecology paradigm offers a framework to consolidate movement research independent of taxon, life-history stage, scale, or discipline. This review applies the framework to movement among life-history stages in marine animals with complex life cycles to consolidate marine movement research and offer insights for scientists working in aquatic and terrestrial realms. Irrespective of data collection or simulation strategy, breaking each life-history stage down into the fundamental units of movement allows each unit to be studied independently or interactively with other units. Understanding these underlying mechanisms of movement within each life-history stage can then be used to construct lifetime movement paths. These paths can allow further investigation of the relative contributions and interdependencies of steps and phases across a lifetime and how these paths influence larger research topics, such as population-level movements.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.014
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.353
Teacher spread0.291 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations65
Published2017
Admission routes2
Has abstractyes

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