ZFIRE: Measuring Electron Density with [O ii] as a Function of Environment at z = 1.62
Bibliographic record
Abstract
Abstract The global star formation rates (SFR) of galaxies at fixed stellar masses increase with redshift and are known to vary with environment up to z ∼ 2. We explore here whether the changes in the SFRs also apply to the electron densities of the interstellar medium by measuring the [O ii] ( , ) ratio for cluster and field galaxies at z ∼ 2. We measure a median electron density of = 366 ± 84 cm for six galaxies (with 1σ scatter = 163 cm ) in the Ultra-Deep Survey (UDS) protocluster at z = 1.62. We find that the median electron density of galaxies in the UDS protocluster environment is three times higher compared to the median electron density of field galaxies ( = 113 ± 63 cm and 1σ scatter = 79 cm ) at comparable redshifts, stellar mass, and SFR. However, we note that a sample of six protocluster galaxies is insufficient to reliably measure the electron density in the average protocluster environment at z ∼ 2. We conclude that the electron density increases with redshift in both cluster and field environments up to z ∼ 2 ( = 30 ± 1 cm for z ∼ 0 to = 254 ± 76 cm for z ∼ 1.5). We find tentative evidence (∼2.6σ) for a possible dependence of electron density on environment, but the results require confirmation with larger sample sizes.
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 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) | 0.002 | 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".