Explaining Climate Variability Vis-A-Vis Spatio-Temporal Interactions in Bangladeshi Exclusive Economic Zone (BEEZ)
Bibliographic record
Abstract
We present an application of time series remote sensing data and climatological information for improved understanding of complexity in the Bangladeshi Exclusive Economic Zone (BEEZ). Three seasonal slots from the annual climate calendar of the temporal slice 1998 to 2009 are selected: December-February [DJF], March-May [MAM] and September-November [SON]) to assess the relationship between marine fish productivity and climate induced variations in a pelagic system. The interdisciplinary approach, explained in two segments, integrates data on climatic variables, oceanography and fish landings. The first segment deals with explanation of spatio-temporal distribution of chlorophyll (Chll-a), derived using Sea-WiFS sensor. This is followed by correlation of Chll-a gradient with marine fish productivity, using a proxy indicator –annual fish landings. The second segment examines the relationship between the Chll-agradient and representative biophysical indicators of the marine environment viz., Sea Surface Temperature [SST] and Sea Surface Height (SSH). The analysis from 1998-2009 indicates decline in Chll-a concentration during SON (0.055 mg/m3), DJF (0.012 mg/m3) and MAM (0.033 mg/m3). Fluctuations in Chll-a is explained in terms of increase in SST’s during DJF and SON and correlation between SST and SSH established in order to compliment the explanation for variability in Chll-a concentration.
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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".