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
Background: For many years, relative values based on 100 quantified cells have been used to assess blood counts in the field of hematology. However, modern blood counting machines have recently made it possible to determine absolute counts. Thus, the current study assessed whether the determination of relative values, based on 100 cells counted, or the determination of absolute values is more accurate in hematology.\nMethods: To calculate the errors of absolute counts and of quotients, we used two independent methods to determine the errors. For the error calculation, we first performed a Gaussian error calculation. Second we identified the errors using daily control checks and examined the high limit of the actual errors (precision) on the Sysmex XE5000 hematological analyzer.\nResults: Our findings indicated that the accuracy of the relative values was always much higher compared to the absolute values.\nConclusion: This finding can be explained by combined errors which affect absolute cell counts and which are directed for all cell counts of one run into the same direction. These types of errors are reduced by quotient formation as shown here for the basophils. The accuracy of the absolute values obtained from the hematology machines of the latest generation was acceptable due to the very high number of cells quantified.
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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it