Evaluation of the Effects of Nanofluid on the Lubricity of Oil-based Mud
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
Fiji Hindi (FH), which developed as a result of plantation contact during the indenture period (1870–1920), is identified by about 37.5% of Fiji’s total popu- lation and by a considerable diasporic Indo-Fijian population as their mother tongue (Fiji Bureau of Statistics, 2007; Mangubhai & Mugler, 2006, p. 97). Although this speech community perceives FH as its heritage language, an iden- tifiable generic term has never been adopted to describe this or any other herit- age language in Fiji.1 FH is the language of girmitya descendants – indentured laborers brought to Fiji by the British to work on sugar and cotton plantations from 1870 to 1920.2 \nThe absence of a label for heritage languages is not unique to Fiji. The defi- nition changes from place to place, differing with community power, language proficiency, and individual heritage (see Fishman, 2001). Hornberger and Wang (2008) define heritage language users as individuals with familial or ancestral ties to a language other than English who exercise their agency in classifying them- selves as users of a heritage language. They state that this determines how these individuals negotiate their identity with other dominant cultures and standard languages they come into contact with (Hornberger & Wang, 2008, p. 6). The critical aspect of this definition requires self-selective membership of the heritage language community.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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