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Record W4401370343 · doi:10.1242/jeb.246613

Narwhal pregnancy test: the power of progesterone

2024· article· en· W4401370343 on OpenAlexaffabout
Alexandra N. Schoen

Bibliographic record

VenueJournal of Experimental Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsPregnancyPregnancy testTest (biology)ObstetricsMedicineInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Have you ever wondered why you feel hungry, or you suddenly crave more sleep? The answer is in part because of tiny messengers called hormones. Hormones are inside all living things and communicate messages, such as how fast a living thing is growing, when animals are awake or when to prepare for pregnancy. Pregnancy is very sensitive to hormone changes and scientists can figure out which stage a pregnancy is at by measuring the levels of certain hormones, such as progesterone, estrogen and testosterone, in animals’ bodies. However, when scientists have tried to infer whether whales are pregnant based on hormone measurements taken from the whales’ blubber, they have based their estimations on hormone measurements from other pregnant mammals, confirming their estimates later by observing whether whale calves were born the following year. Therefore, Justine Hudson, at Fisheries and Oceans Canada, and her colleagues in Nunavut and the University of Manitoba, decided to study hormones in narwhals (Monodon monoceros) to figure out whether these messengers in blubber can tell us about the whales’ pregnancies.As narwhals are sustainably harvested by members of the Inuit communities around Naujaat, Kugaaruk and Pond Inlet, Nunavut, Hudson was able to obtain samples of blubber from 19 female narwhals that they hunted and two that had become trapped in ice, to see whether she could pair blubber hormone measurements from the animals with the condition of their reproductive organs (the ovaries and uteruses), which would show whether a narwhal was pregnant, not pregnant but capable of becoming pregnant, or too young to get pregnant.After studying the narwhals’ reproductive organs, Hudson found that five of the narwhals were pregnant, five were not pregnant but were sexually mature and capable of becoming pregnant, and three were too young to get pregnant. And when she looked at the progesterone levels in the blubber, she found that all of the pregnant whales had higher progesterone measurements (4.90–704.77 ng per gram of blubber) than the whales that were not pregnant (0.62–2.35 ng per gram of blubber). This was an exciting finding because it confirms that blubber progesterone measurements can indicate pregnancy.Because of this discovery, Hudson outlines that any female narwhal with blubber progesterone measurements above 90 ng per gram of blubber can be considered pregnant. In fact, three narwhals in which it was not possible to determine pregnancy from the ovaries or uteruses had progesterone levels greater than 90 ng, meaning that the whales were likely pregnant. Hudson further explains that narwhals with blubber progesterone measurements less than 4 ng per gram of blubber could be classified as not pregnant, while it was not possible for Hudson to determine whether narwhals with blubber progesterone measurements between 4 and 90 ng per gram of blubber were pregnant. Interestingly, one narwhal that was carrying a calf had a blubber progesterone measurement below 90 ng per gram of blubber, which Hudson suggested could be an indication that this narwhal was in the process of miscarrying the pregnancy.The findings of Hudson and colleagues are an exciting step forward in determining whether narwhals are pregnant, which may be applicable to other whale species too. Not only is measuring hormones in blubber a more efficient technique than analyzing the reproductive organs after a whale has died, but this may also help scientists figure out how fast a whale population can grow. Ultimately, knowing this will aid the conservation and management of narwhals and other endangered whale species.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.358
Teacher spread0.333 · 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 designBench or experimental
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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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