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Record W2807250977 · doi:10.1080/11956860.2018.1482699

‘Otter, come out!’: taking away the stone on the southernmost Italian<i>Lutra lutra</i>population

2018· article· en· W2807250977 on OpenAlexvenueno aff
Pasquale Gariano, Alessandro Balestrieri

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsLutraOtterGeographyThreatened speciesPopulationFisheryFlagship speciesEcologyEndangered speciesBiologyHabitatDemography

Abstract

fetched live from OpenAlex

Although Italy is among the European countries with the highest number of threatened species, since the turn of the twenty-first century some flagship species, such as Eurasian otter Lutra lutra and wolf Canis lupus, have started to recover. Since 2003 the otter has been newly recorded on the Sila Massif (S. Italy), where it had been reported to have gone extinct in the 1980s. With the aim of outlining the actual range of this population, in 2014–2017 we monitored otter occurrence on eight major rivers. Spraint surveys were carried out on 18–23 sampling stretches every July. Seven stations (Rivers Savuto and Neto-Lese) showed 75–100% positive surveys, while otters were recorded only once at three of the rivers. Monitoring allowed identifying in the catchments of the Rivers Savuto and Neto, which flow on opposite sides of the Sila Massif, otter core population at the southern edge of its Italian range. We assessed the exceptionality of the recent sightings using a surprise index based on the time distribution of pre-disappearance otter records. Analyses suggest that the otter persisted in the area and went unrecorded during the national survey carried out in 1983–1985, stressing the need for further monitoring at national scale.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.245
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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