Overtones of silicate and aluminate minerals and the 5-8 μm ice bands of deeply embedded objects
Bibliographic record
Abstract
The distinct patterns, relatively low intensities and peak positions of overtone-combination bands of silicates and oxides suggest that the 5–8 μm spectral region can provide clues for the dust composition when near optically thick conditions exist for the 10-μm silicate feature. We present 1000–2500 cm−1 room-temperature laboratory spectra obtained from powders of silicate, aluminate and nitride minerals and silicate glasses. The spectra exhibit overtone absorption bands with mass absorption coefficients ∼100 times weaker than the fundamentals. These data are compared with the 5–8 μm spectra of deeply embedded young stellar objects observed with the Short Wavelength Spectrometer on the Infrared Space Observatory. Fits of the laboratory data to the observations, after subtraction of the 6.0-μm H2O ice feature and the 6.0-μm feature identified with organic refractory material, indicate that crystalline melilite (a silicate) or metamict hibonite (a radiation-damaged crystalline aluminate) may be responsible for much of the 6.9-μm absorption feature in the observations, with melilite providing the best match. A weaker 6.2-μm absorption in the young stellar object spectra is well matched by the spectra of hydrous crystalline amphibole silicates (actinolite and tremolite). Relative abundances of Si–O in room-temperature amphiboles to low-temperature H2O ice are in the range 0.46–3.9 and in melilite are in the range 2.5–8.6. No astronomical feature was matched by the overtones of amorphous silicates because these bands are too broad and peak at the wrong wavelength. Hence, this analysis is consistent with the 10-μm features of these objects being due to a mixture of crystalline and amorphous silicates, rather than only amorphous silicates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".