Rare‐Earth Cobalt Gallides RE<sub>4</sub>Co<sub>3</sub>Ga<sub>16</sub> (RE = Gd–Er, Y): Self‐Interstitial Derivatives of RE<sub>2</sub>CoGa<sub>8</sub>A Tale of Two Polymorphs – Growth and Characterization of α‐LnNiGa<sub>4</sub> (Ln = Y, Gd–Yb) and β‐LnNi<sub>1–<i>x</i></sub>Ga<sub>4</sub> (Ln = Tb–Er)Solution Chemistry Synthesis of Intermetallic Gold–Lithium NanoparticlesMetal Anions in Metal‐Rich Compounds and Polar IntermetallicsRevisiting the Zintl–Klemm Concept: <i>A</i><sub>2</sub>AuBi (<i>A</i> = Li or Na)Crystal Structure and Properties of Yb<sub>5</sub>Ni<sub>4</sub>Ge<sub>10</sub>Magnetism in Giant Unit Cells – Crystal Structure and Magnetic Properties of R<sub>117</sub>Co<sub>52+<i>δ</i></sub>Sn<sub>112+<i>γ</i></sub> (R = Sm, Tb, Dy) (Eur. J. Inorg. Chem. 26/2011)
Bibliographic record
Abstract
Abstract The front cover picture shows the clock tower, the “Campanile”, of Iowa State University where John Corbett did the ground‐breaking research in polar intermetallics that forms the basis of his Viewpoint in this cluster issue. Superimposed on this background are structures and data to visualize the broad scope of topic. The complexity of structure is displayed by the ternary rare‐earth cobalt gallides that contain interstitial atoms (top left, A. Mar et al.), a calcium‐poor intermetallic phase of the Ca/Ni/Ge system (top right from the lab of T. Fässler), and a single crystal of a polymorph of thallium nickel gallide (bottom right, J. Chan et al.). The potentially general synthesis of colloidal nanoparticles – Au 3 Li from the lab of R. E. Schaak – is outlined mid left. The groups of M. H. Whangbo and G. Miller devote their contributions to the theoretical aspects of bonding (depicted top centre, the plots showing Au–Au bonding and antibonding interactions in Dy 2 Au 2 In and mid right, the effects of ionic interactions on the structural properties of isoelectronic intermetallic compounds, respectively). Representative of the range of properties discussed is the magnetic susceptibility of Yb 5 Ni 4 Ge 10 (bottom left, M. G. Kanatzidis et al.). We thank the authors for the use of the graphics from their papers on the cover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".