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Record W2936132271 · doi:10.1136/bmj.l1827

Primary palliative care needs urgent attention

2019· editorial· en· W2936132271 on OpenAlexaff
Sarah Mitchell, Amy Tan, Sébastien Moine, Jeremy Dale, Scott Murray

Bibliographic record

VenueBMJ · 2019
Typeeditorial
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Calgary
FundersNational Institute for Health and Care Research
KeywordsPlumageFeatherBiologyTestosterone (patch)AndrogenEndocrinologyZoologyInternal medicineSexual dimorphismHormoneMedicine

Abstract

fetched live from OpenAlex

ABSTRACT In males it is frequently testosterone (T) that activates the expression of sexually selected morphological and behavioral displays, but the role of T in regulating similar traits in females is less clear. Here, we combine correlational data with results from T and gonadotropin-releasing hormone (GnRH) manipulations in both sexes to assess the role of T in mediating sexually dimorphic coloration and morphology in the red-backed fairy-wren (Malurus melanocephalus). We show that: (1) natural variation in female expression of ornamental traits (darkened bills and red back feathers) is positively associated with age and circulating androgen titres, (2) females have the capacity to express most male-typical traits in response to exogenous T, including carotenoid-pigmented body plumage, shorter feathers, darkened bill and enlarged cloacal protuberance, but (3) appear constrained in production of male-typical melanin-pigmented plumage, and (4) low androgen levels during the pre-nuptial molt, probably because of low ovarian capacity for steroid production (or luteinizing hormone sensitivity), prevent females from developing male-like ornamentation. Thus, females appear to retain molecular mechanisms for hormonally regulated male-typical ornamentation, although these are rarely activated because of insufficient production of the hormonal signal.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.063
GPT teacher head0.362
Teacher spread0.299 · 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
GenreEditorial

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

Citations37
Published2019
Admission routes1
Has abstractyes

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