Effects of urban pollution on stream ecosystem functioning
Bibliographic record
Abstract
tesia egiteko diru-laguntza lortu nuenetik.Emozio askok betetako lau urte izan dira, gogo eta kemen handiz lan egitera behartu nautenak, haserrealdi eta amorrualdi asko ere eragin dizkidatenak, baina batez ere, guzti horren gainetik, erabat disfrutatu ditudan lau urte eder eta oparoak izan dira.Nekez ahaztuko ditut lan hau gauzatzen bizi izandako momentuak eta, tarte honetan nire ondoan izan ditudan pertsona guztiak.Askori zor baitizuet lan hau hasi, eta, batez ere, bukatu izana!Zuetako bakoitzari, bihotz-bihotzez, MILESKER!Lehenik eta behin, zuri eman nahi dizkizut eskerrak Arturo!Asko gustatu zait zurekin lan egitea eta uste dut tesian zehar profesionalki hazteko aukera mugagabeak ez ezik, pertsonalki hazteko aukera ere eman izan didazula.Beraz, bihotzez milesker zurekin tesia egiteko aukera ekaini izanagatik eta hortik aurrera, momentu guztietan nitaz fidatu, nigan sinetsi eta nigatik borrokatu izanagatik!Y la verdad, que ni en sueños habría imaginado que alguien como Sergi Sabater acompañaría a Arturo para respaldar mi trabajo.Aunque muchas veces haya sido
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".