Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
New pre-main-sequence tracks for M less than or equal to 2.5 solar mass as tests of opacities and convection model
1994448 citationsF. D’Antona, I. Mazzitelliprofile →
Citations per year, relative to I. Mazzitelli I. Mazzitelli (= 1×)
peers
F. M. Walter
Countries citing papers authored by I. Mazzitelli
Since
Specialization
Citations
This map shows the geographic impact of I. Mazzitelli's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by I. Mazzitelli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites I. Mazzitelli more than expected).
This network shows the impact of papers produced by I. Mazzitelli. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by I. Mazzitelli. The network helps show where I. Mazzitelli may publish in the future.
Co-authorship network of co-authors of I. Mazzitelli
This figure shows the co-authorship network connecting the top 25 collaborators of I. Mazzitelli.
A scholar is included among the top collaborators of I. Mazzitelli based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with I. Mazzitelli. I. Mazzitelli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Ventura, P., F. D’Antona, & I. Mazzitelli. (2007). The ATON 3.1 stellar evolutionary code. Astrophysics and Space Science. 316(1-4). 93–98.40 indexed citations
Ventura, P., F. D’Antona, & I. Mazzitelli. (2000). Lithium and mass loss in massive AGB stars in the Large Magellanic Cloud. 363. 605–616.3 indexed citations
5.
Mazzitelli, I., F. D’Antona, & P. Ventura. (1999). Full spectrum of turbulence convective mixing. II. Lithium production in AGB stars. 348(3). 846–860.5 indexed citations
6.
D’Antona, F., et al.. (1998). Lithium burning in solar like stars.. Memorie della Societa Astronomica Italiana. 69. 575–578.
7.
D’Antona, F. & I. Mazzitelli. (1998). A Role for Superadiabatic Convection in Low Mass Structures. ASPC. 134. 442.2 indexed citations
8.
D’Antona, F. & I. Mazzitelli. (1997). Evolution of low mass stars.. MmSAI. 68. 807–822.30 indexed citations
D’Antona, F. & I. Mazzitelli. (1989). The fastest evolving white dwarfs. The Astrophysical Journal. 347. 934–934.23 indexed citations
13.
D’Antona, F. & I. Mazzitelli. (1987). Evolutionary times of white dwarfs: long or short?. Frontiers in bioscience. 635–637.1 indexed citations
14.
D’Antona, F. & I. Mazzitelli. (1986). Missing mass in the solar neighborhood: role of brown and white dwarfs. 162. 80–86.3 indexed citations
15.
Mazzitelli, I.. (1979). Solar models, helium content and mixing length.. A&A. 79. 251–253.3 indexed citations
16.
Mazzitelli, I., et al.. (1979). Thermodynamic properties and equations of state for hydrogen and helium in stellar conditions.. A&A. 72. 134–147.2 indexed citations
17.
D’Antona, F. & I. Mazzitelli. (1979). White dwarf external layers. IV. Interpretation of spectra.. 74. 161–171.6 indexed citations
18.
D’Antona, F. & I. Mazzitelli. (1978). The progenitor masses and the luminosity function of white dwarfs.. A&A. 66. 453–461.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.