Bibliographic record
Abstract
Intensive field and model studies were undertaken into the dynamics of the Fly River estuary, Papua New Guinea. The estuary has three dominant channels forming a shallow, fan-shaped delta, and receives a mean freshwater discharge of approximately 6,000 m 3 s -1 with little seasonal variation. The estuary is vertically well-mixed in salinity by strong tidal currents. The saline water is distributed unevenly between the channels. Model studies verified by field data suggest that this due to the dynamics of the estuary which are controlled by shallow water frictional effects that generate higher tidal harmonics, the shoaling of the tidal wave from the funnel shape of the estuary, a low value of the bottom friction coefficient resulting from the presence of fluid mud, and the along-channel water surface gradient. This gradient is in turn controlled by two dominant forcings, namely the freshwater discharge and the dominant offshore trade wind. This gradient is also modulated by the spring-neap cycle of the tidal currents which controls the low-frequency friction coefficient. The absence of strong cross-channel salinity gradients and of axial convergence zones is attributed to enhanced horizontal mixing by the lateral velocity shear due to the sinuosity of the thalweg meandering between numerous islands and shoals.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.991 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier 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".