Bibliographic record
Abstract
The detection of transcript distribution throughout a fixed tissue is a major step in studying the transcriptional activity of target genes and their function. In situ hybridization specifically detects the spatial distribution of RNA transcripts using an antisense RNA probe. This protocol describes the preparation of digoxigenin-labeled antisense RNA probes and their hybridization to complementary mRNA sequences; expression can then be localized using an antibody against digoxigenin conjugated to a chromogenic enzyme. In ants, this method can be applied to visualize cell populations of interest among other populations in a tissue, such as insect ovaries, or in the whole organism, such as insect embryos. Specific markers (e.g., genes known to be expressed in particular clusters of cells) can be cloned and used as probes to study the distribution and development of germline cells (e.g., nanos, vasa), neurons (repo), or limb structures (e.g., distal-less). Various markers might also allow the study of oogenesis (nanos, par-1, oskar), segmentation in the embryo (e.g., engrailed, wingless), or other developmental processes.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".