Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tear film covers the ocular surface and is essential for protecting the eye from the environment, lubricating the ocular surface, maintaining a smooth surface for light refraction, and preserving the health of the conjunctiva and the avascular cornea. The tear film is approximately 3 to 10 μL in volume, 3 μm thick, and secreted at a rate of 1 to 2 μL/min. The pH of tears is approximately 7.45 and ranges between 7.14 to 7.82, depending on diurnal and seasonal influences. Prolonged lid closure, such as during sleep, leads to a buildup of carbon dioxide, thus lowering the pH. It can conceptually be thought of as having three major layers – inner mucin, middle aqueous, and outer lipid layer. The main lacrimal glands produce most of the aqueous tear layer, with small amounts produced by the goblet cells in the conjunctiva and accessory lacrimal glands. The tears then evaporate or are drained through the lacrimal puncta.There are three different types of tears, each with unique biochemistries. Basal tears are typically present on the ocular surface, providing nutrients to the ocular surface, maintaining ocular comfort, and ridding the surface of debris. Reflex tears are those released in response to irritants, including chemicals and foreign bodies. Reflex tears are produced in higher quantities than basal tears and are involved in flushing the ocular surface of irritants. Closed eye tears are those lubricating the eyes during sleep. Some components of the tear film, such as lactoferrin, lipocalin-1, and lysozyme, remain relatively constant between different types of tears. However, the total amount of protein, lipid, and secretory IgA varies between types; protein and lipid content is highest in basal tears. Despite differences in composition, the osmolarities in tear types remain relatively constant.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it