ŠEŠUPĖS IR ŽEIMENOS UPIŲ NUOTĖKIO FORMAVIMOSI IR KAITOS YPATYBĖS
Bibliographic record
Abstract
Straipsnyje nagrinjama nuotkio kaita 2 upiu baseinuose: Sesups ir Žeimenos. Sie baseinai issidste skirtingose Lietuvos teritorijos dalyse (Sesup teka vidurio Lietuvos dalyje, o Žeimena – pietryciu), jie turi skirtingas litologines (Žeimenos upje smlio gruntai užima 76 %, o Sesups tik 10 %) ir fizines-geografines (miskai Žeimenos baseine užima 37 %, o Sesups 17 %) salygas. Straipsnyje analizuojami naujausi hidrologiniai (upiu vandens debitai ir lygiai) ir meteorologiniai (krituliu intensyvumai, trukm, reiskiniu pobudis, bei temperaturos Vilniaus ir Kybartu stotyse) duomenys. Darbo pagrindas susideda is sudarytu hidrografu analizs. Is ju galima spresti apie nuotkio kaita. Hidrografai sudaryti pagal ryskiausius metus, t.y. pagal metus, kai krituliu intensyvumas buvo didžiausias, bei vandens debitas. Žeimenos upje nuotkis labiau islygintas, nei Sesupje. Intensyviausiais metais (vertinant didžiausia krituliu kieki) Sesupje pavasario potvynis prasidjo labai anksti, jau sausio mnesi debitas žymiai padidjo, Žeimenos upje aiskus debito didjimas buvo tik kovo mnesi, nors vertinant meteorologines salygas, krituliu intensyvumai ir vidutins dienos temperaturos buvo maždaug panasus. Tai ir rodo, kad didžiausia itaka daro baseino issidstymo salygos. Darbo tikslas – ivertinti nuotkio kaita pasirinktose objektuose, pasinaudojus naujausiomis duomenimis.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".