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Record W4379531912 · doi:10.3406/linly.2017.17856

Contribution à l’inventaire des Orchidées de la wilaya de Sétif (nord-est de l’Algérie)

2017· article· en· W4379531912 on OpenAlexaff
Amar Madoui, Khellaf Rebbas, Rabah Bounar, Mohamed Djamel, Miara Véla, Errol Véla

Bibliographic record

VenueBulletin de la société linnéenne de Lyon · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSubspeciesThreatened speciesGeographyOrchidaceaeFloristicsBiodiversityEcologyHabitatBiologySpecies richness

Abstract

fetched live from OpenAlex

Contribution to the list of Orchids in the Sétif province (north-eastern Algeria). The Sétif province is renowned for its floristic and ecological diversity. This biodiversity demands a proper field investigation to locate, identify and map the region’s rare and threatened species. Of these species, orchids remain the most symbolic, not only because of their beauty, but also because of the fragility of their habitats and the threats to their preservation. In this paper, the authors describe the new orchid sites discovered in the Sétif province. Twenty-six (26) orchid species and subspecies were observed, identified and geolocated. Of these, Orchis purpurea subsp. purpurea, Dactylorhiza maculata subsp. battandieri, Orchis mascula subsp. maghrebiana, Ophrys omegaifera subsp. hayekii and Ophrys marmorata subsp. marmorata were observed for the first time in the region. Dactylorhiza elata s. l. is relatively abundant, but only in wet areas that have suffered recent human deterioration, thus raising fears about its conservation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.277
Teacher spread0.261 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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