Bibliographic record
Abstract
Birds’ nest swiflets in Niah Caves were first counted in 1935 by E. Banks, the then curator of the Sarawak Museum. The population then was a staggering 1.7 million although no differentiation was made between the glossy-moss and black-nests. Other counts were made by Medway in 1958 and 1962 where the population was 1.5 million. Huntings Technical Services Consultant counted some 1.3 million swiftlets in Niah in 1974 at a time when large scale oil palm plantations were established along the Miri-Bintulu Road. Since 1987 a number of counts were made jointly by the Forestry and Sarawak Museum Departments. The sharp increase in value of birds’ nest in the late 1980’s has caused a serious increase in poaching activities on the nests in Niah Caves. Despite the first ban of nest harvest in Niah Caves from 1989 to 1991, the swiftlet population did not increase in subsequent years. In 1993, a second two-year ban on nest harvests was placed on birds’ nests in Niah Caves. This second ban expired in the third quarter of 1995. During the ban period, there were five groups of Forestry Enforcement and Field Force staff stationed at each of the cave entrances in Niah complex.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".