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Record W2760491259 · doi:10.32491/jii.v15i2.63

Penggunaan otolit untuk penentuan umur dan waktu pemijahan ikan red devil, Amphilophus labiatus [Gunther, 1864] di Waduk Sermo, Yogyakarta [The use of otolith to determine age and spawning time of red devil Amphilophus labiatus [Gunther, 1864] in Sermo Reservoir, Yogyakarta]

2015· article· ms· W2760491259 on OpenAlexaboutno aff
Sitty Ainsyah Habibie, nFN Djumanto, nFN Rustadi

Bibliographic record

VenueJurnal Iktiologi Indonesia · 2015
Typearticle
Languagems
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsnot available
Fundersnot available
KeywordsOtolithHatchingJuvenileAnimal scienceFisheryBiologyFish <Actinopterygii>Ecology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the age and spawning time of red devil (Amphilophus labiatus) based on the observation of the daily increment of otolith in juvenile fish. The sampling was conducted from October 2013 to March 2014. The juveniles were collected biweekly using a hapa net and scoop net. To determine the first formation of daily increment, the brood stock of tilapia (Oreochromis sp.) was spawned in captivity. A total of five larvae was taken every day from hatching day until the 18 days old, and then the larvae were taken every two days. Sagittal otoliths were collected by putting the larva into a 5.25% NaOCl solution. The left otolith was attached to the object glass using Bucherer cement, and then dropped with Canada balsam and closed by cover glass. The numbers of daily increment were observed by using a microscope with a magnification 100-400 X. The age was determined based on the number of daily increment plus the first time of ring formation. Spawning time was determined by back calculation of the sampling time, plus age and incubation period. The result showed that there were 130 individual juveniles collected ranged from 7.0 to 14.6 mm total length (TL). The formation of daily increment on 69 sagittal otolits was observed. The first sagittal increment was formed on the third day after hatching and the forming of the increment was daily. The ages of juvenile red devil were between 9-28 days old and majority of the larvae in 17 days. Red devil spawned coincided with the new moon phase and high intensity of rainfalls. Abstrak Penelitian ini bertujuan untuk menentukan umur dan waktu pemijahan ikan red devil berdasarkan jumlah lingkaran ha-rian pada otolit juwana ikan. Penelitian dilakukan dari bulan Oktober 2013 hingga Maret 2014. Pengambilan contoh ikan dilakukan tiap dua mingguan dengan menggunakan waring dan seser. Guna menentukan awal pembentukan ling-karan harian pada otolit, maka dilakukan pengamatan terhadap otolit larva ikan nila albino (Oreochromis sp.) hasil te-tasan. Sebanyak lima ekor larva diambil tiap hari sejak menetas hingga umur 18 hari, selanjutnya larva diambil tiap dua hari. Otolit sagitta diambil dengan cara merendam ikan menggunakan larutan NaOCl 5,25%. Otolit yang tertinggal se-lanjutnya direkatkan pada objek gelas menggunakan semen bucherer, dan ditutup menggunakan kanada balsam serta kaca penutup. Pengamatan jumlah lingkaran harian menggunakan mikroskop dengan pembesaran 100-400 kali. Umur larva ikan ditentukan berdasarkan jumlah lingkaran pada otolit ditambah waktu terbentuknya lingkaran pertama kali se-jak penetasan. Waktu pemijahan larva ikan diduga dengan perhitungan balik dari waktu sampling ditambah umur dan masa pengeraman. Hasil penelitian menunjukkan sebanyak 130 juwana ikan red devil dengan kisaran panjang 7,0-14,6 mm berhasil dikumpulkan dan sebanyak 69 otolit berhasil diamati. Pembentukan lingkaran pada otolit nila albino dimu-lai pada hari ketujuh setelah pemijahan atau hari ketiga setelah penetasan dan terbentuk secara harian. Juwana ikan red devil berumur 9-28 hari, yang didominasi oleh larva berumur 17 hari. Ikan red devil memijah setiap bulan dari November hingga Februari, bertepatan dengan bulan fase gelap dan curah hujan tinggi.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.262
Teacher spread0.153 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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