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Record W2943482192

Adaptive peculiarities of pigmentation in the redbelly rock agama Paralaudakia erythrogastra (Nikolsky, 1896) (Agamidae)

2018· article· en· W2943482192 on OpenAlexaboutno aff
А. Б. Киладзе, О. Ф. Чернова

Bibliographic record

VenueUkrainian Journal of Ecology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Biological Research in Conflict Zones
Canadian institutionsnot available
Fundersnot available
KeywordsAgamidaeZoologyBiologyHabitatGeographyEcologyLizard
DOInot available

Abstract

fetched live from OpenAlex

The aim of the study is to show the level of adaptation of reptiles to a natural habitat through skin pigmentation, using definitions of RGB coordinates, which are the main colour characteristics. Here, the coloration of the Redbelly Rock (Khorasan) Agama’s skin is compared with the shades of the enclosure simulating the mountain substrate, which is the natural habitat of the lizard. The following average parameters of RGB coordinates have been determined: imitation of the mountain substrate-RGB (170, 139, 128); the agama’s skin covering-RGB (177, 136, 100). The data obtained are indicative of the protective coloration in the agama. Keywords: Redbelly rock (Khorasan) agama; skin; pigmentation; protective coloration; habitat References: Ananjeva, N. B., Orlov, N. L., Khalikov, R. G., Darevsky, I. S., Ryabov, S. A., & Barabanov, A. V. (2006). The Reptiles of Northern Eurasia. Taxonomic Diversity, Distribution, Conservation Status. Sofia, PENSOFT Publishers, Sofia, Bulgaria. Bagnara, J. T., Taylor, J. D., & Hadley, M. E. (1968). The Dermal Chromatophore Unit. The Journal of Cell Biology, 38(1), 67-79. Baig, K. J., Wagner, P., Ananjeva, N. B., & Bohme, W. (2012). A morphology-based taxonomic revision of Laudakia Gray, 1845 (Squamata: Agamidae). Vertebrate Zoology, 62(2), 213-260. Bannikov, A. G., Darevsky, I. S., Ishchenko, V. G., Rustamov, A. K., & Shcherbak, N. N. (1977). The Field Guide of Amphibians and Reptiles of the Fauna of the USSR. Moscow, Prosveshchenie. (in Russian) Cott, H. (1950). Adaptive Coloration in Animals. Moscow, Foreign Literature Publishing House. (in Russian) Define the dominant colours of the image online (2017). Available at: https://www.imgonline.com.ua/get-dominant-colors.php. Forsyth, I. (2014). The practice and poetics of fieldwork: Hugh Cott and the study of camouflage. Journal of Historical Geography, 43, 128-137. Miura, K. (2016). Bioimage data analysis. Weinheim, Wiley-VCH Verlag GmbH & Co. KGaA. Morrison, R. L., Sherbrooke, W. C., & Frost-Mason, S. K. (1996). Temperature-sensitive, physiologically active iridophores in the lizard Urosaurus ornatus: an ultrastructural analysis of color change. Copeia, 1996(4), 804-812. Panov, E. N., & Zykova, L. Yu. (2016). Rock Agamas of Eurasia. Moscow, KMK Scientific Press. Papenfuss, T., Shafiei Bafti, S., Ananjeva, N., & Orlov, N. (2010). Paralaudakia erythrogaster. The IUCN Red List of Threatened Species 2010: e.T164645A5915348. http://dx.doi.org/10.2305/IUCN.UK.2010-4.RLTS.T164645A5915348.en. Downloaded on 07 February 2017. Saenko, S. V., Teyssier, J., van der Marel, D., &Milinkovitch, M. C. (2013). Precise colocalization of interacting structural and pigmentary elements generates extensive color pattern variation in Phelsuma lizards. BMC Biology, 11, 105. Saxena, R. K., Saxena, S. (2008). Comparative Anatomy of Vertebrates. Kent, Anshan Limited. Stuart-Fox, D., Moussalli, A. (2009). Camouflage, communication and thermoregulation: lessons from colour changing organisms. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1516), 463-470. Taylor, J. D., & Hadley, M. E. (1970). Chromatophores and color change in the lizard, Anolis carolinensis. Zeitschrift Fur Zellforschung Und Mikroskopische Anatomie, 104(2), 282-294. Teyssier, J., Saenko, S. V., van der Marel, D., & Milinkovitch, M. C. (2015). Photonic crystals cause active colour change in chameleons. Nature Communications 6: 6368 doi: 10.1038/ncomms7368.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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 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
Published2018
Admission routes1
Has abstractyes

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