Bibliographic record
Abstract
and sediment type, he concluded that depth was a primary factor and that sediment type was of secondary importance. Mahon and Smith (1989) looked for interactions between sediment characteristics, water depth, bottom temperature, and bottom salinity but concluded that assemblages were related more to depth than to other attributes. Scott (1982) reported that although fish distributions were related to sediment types, the latter was related to depth. Studies of other fishes indicated that temperature and salinity are important; Jahn and Backus (1976), using salinity and temperature to characterize slope waters, the Gulf Stream, and northern and southern Sargasso Sea waters in the Atlantic Ocean, concluded that mesopelagic fishes associated with slope and Gulf Stream waters were distinct and different from fish assemblages associated with the other two water masses. Bianchi (1992, a and b) determined that water depth, bottom temperature, bottom salinity, and The distribution and abundance of commercially important demersal fishes inhabiting temperate and tropical seas are relatively well studied (e.g. Pearcy, 1978; Mahon and Smith, 1989; Weinberg, 1994). Results from such studies have been used to examine relationships between environmental factors and fish assemblage distributions. Important environmental variables that have been identified include sediment type, water depth, bottom temperature, and bottom salinity. Overholtz and Tyler (1985) found that six species assemblages on Georges Bank, northwestemAtlantic, remained consistent over depth for a number of years. Fargo and Tyler (1991), sampling at depths of 18-240 m in Hecate Strait off British Columbia, found four species assemblages separated by depth. Pearcy (1978) described shallow and deep demersal fish assemblages in the northeast Pacific Ocean off the coast of Oregon at depths ranging from 70 to 102 m. Although there was an interaction between depth Willard E. Barber
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 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) | 1.000 | 1.000 |
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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".