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Record W2154459210 · doi:10.1242/jeb.077602

BENEFITS OF BEING A MUMMY'S BOY

2012· article· en· W2154459210 on OpenAlexaffabout
Constance M. O’Connor

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWhaleReproductionDemographyReproductive successBiologyEcologyPopulationSociology

Abstract

fetched live from OpenAlex

As humans, we often take grandmothers for granted. Dispensing baked goods and well-intentioned advice, grandmothers are an integral part of our human societies. However, human grandmothers represent an incredibly rare phenomenon among all living things: they are post-reproductive. The vast majority of plants and animals do not live past their reproductive years. Dramatic examples of a single reproductive bout followed by death within days, such as occurs in Pacific salmon, are far more common in the animal kingdom than the curious case of post-reproductive individuals. The rarity of post-reproductive individuals has led many researchers to ponder, what is the purpose of life after reproduction?Human females have the longest post-reproductive period in the animal kingdom, but female killer whales are close runners-up. Female killer whales cease reproducing at approximately 30 or 40 years of age, and yet can live for approximately 90 years, with a post-reproductive period that rivals humans. In a 36-year long-term study of over 500 resident killer whales off the coast of Washington, USA, and British Columbia, Canada, a group of researchers have uncovered convincing evidence that there is an evolutionary benefit to this extended post-reproductive period.Led by Emma Foster from the University of Exeter, UK, the group of scientists from Exeter, the University of York, UK, the Center for Whale Research, USA, and the Pacific Biological Station, Canada, tracked family groups of whales for multiple generations. The scientists identified mother–offspring pairs by recording small calves with their mothers. As there is no dispersal in killer whale family groups, the disappearance of an individual from a family group indicates that the individual has died. From this long-term data set of family trees and family member survival, the researchers were able to assess the consequences of a mother's death on the survival of her sons and daughters.The group of scientists found that for an adult female killer whale, there is no benefit of having her mother around. Once grown, female survival is independent of whether her mother is still alive. However, having a surviving post-reproductive mother significantly increased survival for sons, even for 35-year-old, fully grown male killer whales. Killer whale mothers help their grown sons forage, and they also form alliances with their sons, defending them during fights with other whales. It seems that with mum around to help feed and defend her sons, these males have a better chance of long-term survival even as adults.From an evolutionary perspective, this favouritism may have arisen because resident killer whales live in permanent matrilineal family groups, where grandmothers, mothers, sons and daughters live and hunt together. However, males will mate with females from other family groups. Thus, although a grandmother must incur the costs of raising her daughter's offspring within the family group, other killer whale families raise the offspring of her sons. For a mother, the fewest costs and greatest benefits occur when she ensures that her sons survive and reproduce, rather than by producing more offspring of her own or by favouring her daughters. For these adult males, it seems that there are distinct benefits of having a post-reproductive mother around and being a mummy's boy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0380.005

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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes2
Has abstractyes

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