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The amount of time female pumas Puma concolor spend with their kittens

2008· article· en· W2180707851 on OpenAlexaboutno aff
John W. Laundré, Lucina Hernández

Bibliographic record

VenueWildlife Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersU.S. Bureau of Land ManagementIdaho State UniversityEppley Foundation for ResearchMassachusetts Department of Fish and GameIdaho Department of Fish and GameEarthwatch Institute
KeywordsKittenPumaAnimal scienceBiologyZoologyCATSMedicineInternal medicine

Abstract

fetched live from OpenAlex

In the sport hunting of pumas Puma concolor, most states and provinces of the United States and Canada do not allow the killing of females with kittens. However, female pumas can be away from their kittens and, if detected by hunters at these times, can be mistakenly killed. To assess the extent to which females with kittens might mistakenly be killed we need to have estimates of the percent of time female pumas are with their kittens on a daily basis. Previous estimates range within 52-83%, but are based on simultaneous locations taken during the day when pumas are least active. To provide a more accurate assessment of the amount of time females spend with their young, we analyzed telemetry data collected over 24, 24-hour blocks for 15 females and their kittens. We collected data from June to September during 1989-1999 in southeastern Idaho and northwestern Utah. We found that females with 7-12 month-old kittens were within 200 m of their kittens an average 16.2±3.8% (N=12) of the time. These females were >1.0 km from their kittens 30.9±6.7% of the time. Three females with kittens in dens were near their dens 10.3, 12.2 and 2.3% of the time. Females were within 200 m of their kittens the least amount of time (5.1±2.1%, N=8) during 11:00-14:30 and the most amount of time during 23:00-01:30 (29.4±3.0, N=6) and 07:00–10:30 (23.2±3.1%). We conclude that the probability that a hunter would encounter a female without her kittens was >80%.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.872

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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.

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

Citations10
Published2008
Admission routes1
Has abstractyes

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