La financiación de la calidad de vida de las personas mayores: renta vitalicia y contrato de alimentos
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
General patterns of injury in the Alberta workplace are reflected in figures from the Workers' Compensation Board, which reliably enumerates acute injuries but not necessarily chronic musculoskeletal conditions. Roughly one-quarter of these injuries are to the back and neck. The absolute number of injuries is of interest in terms of the overall problem of injury in the workplace, but the rate of injury is used to set priorities for intervention among industries. The injury rate identifies industries at greatest risk given the size and activity of their workforce. Using rates, industries can be classified as high or low risk. Over time, claim rates for the major industrial sectors have been fairly stable. Rates from 1987 and 1988 were used in setting provincial government initiatives to control injury frequency and severity and are examined in this report. Construction is clearly a high-risk industry. It involves many trades and operations that have an inherently high risk. The situation is different for the manufacturing sector. This also has a very high injury experience. However, unlike construction, the risk is concentrated in one sub-industry-meat and poultry packing. As a single sector, oil and gas has a low risk. When the industry is broken down into its component functions, however, the oil and gas exploration, drilling and servicing components are clearly out of line with the rest of the sector. Risk is highly concentrated in these sub-industries. Data from Alberta confirm that smaller employers generally have a higher injury risk than larger employers.(ABSTRACT TRUNCATED AT 250 WORDS)
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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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