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Record W4299379242 · doi:10.17615/3vn4-5z28

Evidence for Geomagnetic Imprinting as a Homing Mechanism in Pacific Salmon

2021· article· en· W4299379242 on OpenAlexaboutno aff
Kenneth J. Lohmann, Nathan F. Putman, David L. G. Noakes, Thomas P. Quinn, A. Peter Klimley, Emily M. Putman

Bibliographic record

VenueUNC Libraries · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersOregon Sea Grant, Oregon State UniversityOregon State UniversityCalifornia Department of Fish and WildlifeNational Science Foundation
KeywordsImprinting (psychology)Homing (biology)Earth's magnetic fieldMechanism (biology)GeologyOceanographyBiologyGeophysicsPhysicsGeneticsMagnetic fieldGene

Abstract

fetched live from OpenAlex

In the final phase of their spawning migration, Pacific salmon use chemical cues to identify their home river, but how they navigate from the open ocean to the correct coastal area has remained enigmatic [1]. To test the hypothesis that salmon imprint on the magnetic field that exists where they first enter the sea and later seek the same field upon return [2-4], we analyzed a 56-year fisheries data set on Fraser River sockeye salmon, which must detour around Vancouver Island to approach the river through either a northern or southern passageway [5, 6]. We found that the proportion of salmon using each route was predicted by geomagnetic field drift: the more the field at a passage entrance diverged from the field at the river mouth, the fewer fish used the passage. We also found that more fish used the northern passage in years with warmer sea surface temperature (presumably because fish were constrained to more northern latitudes). Field drift accounted for 16% of the variation in migratory route used, temperature 22%, and the interaction between these variables 28%. These results provide the first empirical evidence of geomagnetic imprinting in any species and imply that forecasting salmon movements is possible using geomagnetic models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.235
Teacher spread0.207 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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